verticapy.machine_learning.vertica.tree.DecisionTreeClassifier#
- class verticapy.machine_learning.vertica.tree.DecisionTreeClassifier(name: str = None, overwrite_model: bool = False, max_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: int | float | Decimal = 1000000000.0, max_depth: int = 100, min_samples_leaf: int = 1, min_info_gain: int | float | Decimal = 0.0, nbins: int = 32)#
A DecisionTreeClassifier consisting of a single tree.
Parameters#
- name: str, optional
Name of the model. The model is stored in the database.
- overwrite_model: bool, optional
If set to
True
, training a model with the same name as an existing model overwrites the existing model.- max_features: str / int, optional
The number of randomly chosen features from which to pick the best feature to split on a given tree node. It can be an integer or one of the two following methods.
- auto:
square root of the total number of predictors.
- max:
number of predictors.
- max_leaf_nodes: PythonNumber, optional
The maximum number of leaf nodes for a tree in the forest, an integer between 1 and 1e9, inclusive.
- max_depth: int, optional
The maximum depth for growing each tree, an integer between 1 and 100, inclusive.
- min_samples_leaf: int, optional
The minimum number of samples each branch must have after a node is split, an integer between 1 and 1e6, inclusive. Any split that results in fewer remaining samples is discarded.
- min_info_gain: PythonNumber, optional
The minimum threshold for including a split, a float between 0.0 and 1.0, inclusive. A split with information gain less than this threshold is discarded.
- nbins: int, optional
The number of bins to use for continuous features, an integer between 2 and 1000, inclusive.
Attributes#
Many attributes are created during the fitting phase.
- trees_: list of one BinaryTreeClassifier
One tree model which is instance of
BinaryTreeClassifier
. It possess various attributes. For more detailed information, refer to the documentation forBinaryTreeClassifier()
.- features_importance_: numpy.array
The importance of features. It is calculated using the MDI (Mean Decreased Impurity). To determine the final score, VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them. It is necessary to use the
features_importance()
method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.- classes_: numpy.array
The classes labels.
Note
All attributes can be accessed using the
get_attributes()
method.Note
Several other attributes can be accessed by using the
get_vertica_attributes()
method.Examples#
The following examples provide a basic understanding of usage. For more detailed examples, please refer to the Machine Learning or the Examples section on the website.
Important
Many tree-based models inherit from the
RandomForest
base class, and it’s recommended to use it directly for access to a wider range of options.Load data for machine learning#
We import
verticapy
:import verticapy as vp
Hint
By assigning an alias to
verticapy
, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions fromverticapy
are used as intended without interfering with functions from other libraries.For this example, we will use the winequality dataset.
import verticapy.datasets as vpd data = vpd.load_winequality()
123fixed_acidityNumeric(8)123volatile_acidityNumeric(9)123citric_acidNumeric(8)123residual_sugarNumeric(9)123chloridesFloat(22)123free_sulfur_dioxideNumeric(9)123total_sulfur_dioxideNumeric(9)123densityFloat(22)123pHNumeric(8)123sulphatesNumeric(8)123alcoholFloat(22)123qualityInteger123goodIntegerAbccolorVarchar(20)1 3.8 0.31 0.02 11.1 0.036 20.0 114.0 0.99248 3.75 0.44 12.4 6 0 white 2 3.9 0.225 0.4 4.2 0.03 29.0 118.0 0.989 3.57 0.36 12.8 8 1 white 3 4.2 0.17 0.36 1.8 0.029 93.0 161.0 0.98999 3.65 0.89 12.0 7 1 white 4 4.2 0.215 0.23 5.1 0.041 64.0 157.0 0.99688 3.42 0.44 8.0 3 0 white 5 4.4 0.32 0.39 4.3 0.03 31.0 127.0 0.98904 3.46 0.36 12.8 8 1 white 6 4.4 0.46 0.1 2.8 0.024 31.0 111.0 0.98816 3.48 0.34 13.1 6 0 white 7 4.4 0.54 0.09 5.1 0.038 52.0 97.0 0.99022 3.41 0.4 12.2 7 1 white 8 4.5 0.19 0.21 0.95 0.033 89.0 159.0 0.99332 3.34 0.42 8.0 5 0 white 9 4.6 0.445 0.0 1.4 0.053 11.0 178.0 0.99426 3.79 0.55 10.2 5 0 white 10 4.6 0.52 0.15 2.1 0.054 8.0 65.0 0.9934 3.9 0.56 13.1 4 0 red 11 4.7 0.145 0.29 1.0 0.042 35.0 90.0 0.9908 3.76 0.49 11.3 6 0 white 12 4.7 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 10.5 5 0 white 13 4.7 0.455 0.18 1.9 0.036 33.0 106.0 0.98746 3.21 0.83 14.0 7 1 white 14 4.7 0.6 0.17 2.3 0.058 17.0 106.0 0.9932 3.85 0.6 12.9 6 0 red 15 4.7 0.67 0.09 1.0 0.02 5.0 9.0 0.98722 3.3 0.34 13.6 5 0 white 16 4.7 0.785 0.0 3.4 0.036 23.0 134.0 0.98981 3.53 0.92 13.8 6 0 white 17 4.8 0.13 0.32 1.2 0.042 40.0 98.0 0.9898 3.42 0.64 11.8 7 1 white 18 4.8 0.17 0.28 2.9 0.03 22.0 111.0 0.9902 3.38 0.34 11.3 7 1 white 19 4.8 0.21 0.21 10.2 0.037 17.0 112.0 0.99324 3.66 0.48 12.2 7 1 white 20 4.8 0.225 0.38 1.2 0.074 47.0 130.0 0.99132 3.31 0.4 10.3 6 0 white 21 4.8 0.26 0.23 10.6 0.034 23.0 111.0 0.99274 3.46 0.28 11.5 7 1 white 22 4.8 0.29 0.23 1.1 0.044 38.0 180.0 0.98924 3.28 0.34 11.9 6 0 white 23 4.8 0.33 0.0 6.5 0.028 34.0 163.0 0.9937 3.35 0.61 9.9 5 0 white 24 4.8 0.34 0.0 6.5 0.028 33.0 163.0 0.9939 3.36 0.61 9.9 6 0 white 25 4.8 0.65 0.12 1.1 0.013 4.0 10.0 0.99246 3.32 0.36 13.5 4 0 white 26 4.9 0.235 0.27 11.75 0.03 34.0 118.0 0.9954 3.07 0.5 9.4 6 0 white 27 4.9 0.33 0.31 1.2 0.016 39.0 150.0 0.98713 3.33 0.59 14.0 8 1 white 28 4.9 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 10.4666666666667 5 0 white 29 4.9 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 10.4666666666667 5 0 white 30 4.9 0.345 0.34 1.0 0.068 32.0 143.0 0.99138 3.24 0.4 10.1 5 0 white 31 4.9 0.345 0.34 1.0 0.068 32.0 143.0 0.99138 3.24 0.4 10.1 5 0 white 32 4.9 0.42 0.0 2.1 0.048 16.0 42.0 0.99154 3.71 0.74 14.0 7 1 red 33 4.9 0.47 0.17 1.9 0.035 60.0 148.0 0.98964 3.27 0.35 11.5 6 0 white 34 5.0 0.17 0.56 1.5 0.026 24.0 115.0 0.9906 3.48 0.39 10.8 7 1 white 35 5.0 0.2 0.4 1.9 0.015 20.0 98.0 0.9897 3.37 0.55 12.05 6 0 white 36 5.0 0.235 0.27 11.75 0.03 34.0 118.0 0.9954 3.07 0.5 9.4 6 0 white 37 5.0 0.24 0.19 5.0 0.043 17.0 101.0 0.99438 3.67 0.57 10.0 5 0 white 38 5.0 0.24 0.21 2.2 0.039 31.0 100.0 0.99098 3.69 0.62 11.7 6 0 white 39 5.0 0.24 0.34 1.1 0.034 49.0 158.0 0.98774 3.32 0.32 13.1 7 1 white 40 5.0 0.255 0.22 2.7 0.043 46.0 153.0 0.99238 3.75 0.76 11.3 6 0 white 41 5.0 0.27 0.32 4.5 0.032 58.0 178.0 0.98956 3.45 0.31 12.6 7 1 white 42 5.0 0.27 0.32 4.5 0.032 58.0 178.0 0.98956 3.45 0.31 12.6 7 1 white 43 5.0 0.27 0.4 1.2 0.076 42.0 124.0 0.99204 3.32 0.47 10.1 6 0 white 44 5.0 0.29 0.54 5.7 0.035 54.0 155.0 0.98976 3.27 0.34 12.9 8 1 white 45 5.0 0.3 0.33 3.7 0.03 54.0 173.0 0.9887 3.36 0.3 13.0 7 1 white 46 5.0 0.31 0.0 6.4 0.046 43.0 166.0 0.994 3.3 0.63 9.9 6 0 white 47 5.0 0.33 0.16 1.5 0.049 10.0 97.0 0.9917 3.48 0.44 10.7 6 0 white 48 5.0 0.33 0.16 1.5 0.049 10.0 97.0 0.9917 3.48 0.44 10.7 6 0 white 49 5.0 0.33 0.16 1.5 0.049 10.0 97.0 0.9917 3.48 0.44 10.7 6 0 white 50 5.0 0.33 0.18 4.6 0.032 40.0 124.0 0.99114 3.18 0.4 11.0 6 0 white 51 5.0 0.33 0.23 11.8 0.03 23.0 158.0 0.99322 3.41 0.64 11.8 6 0 white 52 5.0 0.35 0.25 7.8 0.031 24.0 116.0 0.99241 3.39 0.4 11.3 6 0 white 53 5.0 0.35 0.25 7.8 0.031 24.0 116.0 0.99241 3.39 0.4 11.3 6 0 white 54 5.0 0.38 0.01 1.6 0.048 26.0 60.0 0.99084 3.7 0.75 14.0 6 0 red 55 5.0 0.4 0.5 4.3 0.046 29.0 80.0 0.9902 3.49 0.66 13.6 6 0 red 56 5.0 0.42 0.24 2.0 0.06 19.0 50.0 0.9917 3.72 0.74 14.0 8 1 red 57 5.0 0.44 0.04 18.6 0.039 38.0 128.0 0.9985 3.37 0.57 10.2 6 0 white 58 5.0 0.455 0.18 1.9 0.036 33.0 106.0 0.98746 3.21 0.83 14.0 7 1 white 59 5.0 0.55 0.14 8.3 0.032 35.0 164.0 0.9918 3.53 0.51 12.5 8 1 white 60 5.0 0.61 0.12 1.3 0.009 65.0 100.0 0.9874 3.26 0.37 13.5 5 0 white 61 5.0 0.74 0.0 1.2 0.041 16.0 46.0 0.99258 4.01 0.59 12.5 6 0 red 62 5.0 1.02 0.04 1.4 0.045 41.0 85.0 0.9938 3.75 0.48 10.5 4 0 red 63 5.0 1.04 0.24 1.6 0.05 32.0 96.0 0.9934 3.74 0.62 11.5 5 0 red 64 5.1 0.11 0.32 1.6 0.028 12.0 90.0 0.99008 3.57 0.52 12.2 6 0 white 65 5.1 0.14 0.25 0.7 0.039 15.0 89.0 0.9919 3.22 0.43 9.2 6 0 white 66 5.1 0.165 0.22 5.7 0.047 42.0 146.0 0.9934 3.18 0.55 9.9 6 0 white 67 5.1 0.21 0.28 1.4 0.047 48.0 148.0 0.99168 3.5 0.49 10.4 5 0 white 68 5.1 0.23 0.18 1.0 0.053 13.0 99.0 0.98956 3.22 0.39 11.5 5 0 white 69 5.1 0.25 0.36 1.3 0.035 40.0 78.0 0.9891 3.23 0.64 12.1 7 1 white 70 5.1 0.26 0.33 1.1 0.027 46.0 113.0 0.98946 3.35 0.43 11.4 7 1 white 71 5.1 0.26 0.34 6.4 0.034 26.0 99.0 0.99449 3.23 0.41 9.2 6 0 white 72 5.1 0.29 0.28 8.3 0.026 27.0 107.0 0.99308 3.36 0.37 11.0 6 0 white 73 5.1 0.29 0.28 8.3 0.026 27.0 107.0 0.99308 3.36 0.37 11.0 6 0 white 74 5.1 0.3 0.3 2.3 0.048 40.0 150.0 0.98944 3.29 0.46 12.2 6 0 white 75 5.1 0.305 0.13 1.75 0.036 17.0 73.0 0.99 3.4 0.51 12.3333333333333 5 0 white 76 5.1 0.31 0.3 0.9 0.037 28.0 152.0 0.992 3.54 0.56 10.1 6 0 white 77 5.1 0.33 0.22 1.6 0.027 18.0 89.0 0.9893 3.51 0.38 12.5 7 1 white 78 5.1 0.33 0.22 1.6 0.027 18.0 89.0 0.9893 3.51 0.38 12.5 7 1 white 79 5.1 0.33 0.22 1.6 0.027 18.0 89.0 0.9893 3.51 0.38 12.5 7 1 white 80 5.1 0.33 0.27 6.7 0.022 44.0 129.0 0.99221 3.36 0.39 11.0 7 1 white 81 5.1 0.35 0.26 6.8 0.034 36.0 120.0 0.99188 3.38 0.4 11.5 6 0 white 82 5.1 0.35 0.26 6.8 0.034 36.0 120.0 0.99188 3.38 0.4 11.5 6 0 white 83 5.1 0.35 0.26 6.8 0.034 36.0 120.0 0.99188 3.38 0.4 11.5 6 0 white 84 5.1 0.39 0.21 1.7 0.027 15.0 72.0 0.9894 3.5 0.45 12.5 6 0 white 85 5.1 0.42 0.0 1.8 0.044 18.0 88.0 0.99157 3.68 0.73 13.6 7 1 red 86 5.1 0.42 0.01 1.5 0.017 25.0 102.0 0.9894 3.38 0.36 12.3 7 1 white 87 5.1 0.47 0.02 1.3 0.034 18.0 44.0 0.9921 3.9 0.62 12.8 6 0 red 88 5.1 0.51 0.18 2.1 0.042 16.0 101.0 0.9924 3.46 0.87 12.9 7 1 red 89 5.1 0.52 0.06 2.7 0.052 30.0 79.0 0.9932 3.32 0.43 9.3 5 0 white 90 5.1 0.585 0.0 1.7 0.044 14.0 86.0 0.99264 3.56 0.94 12.9 7 1 red 91 5.2 0.155 0.33 1.6 0.028 13.0 59.0 0.98975 3.3 0.84 11.9 8 1 white 92 5.2 0.155 0.33 1.6 0.028 13.0 59.0 0.98975 3.3 0.84 11.9 8 1 white 93 5.2 0.16 0.34 0.8 0.029 26.0 77.0 0.99155 3.25 0.51 10.1 6 0 white 94 5.2 0.17 0.27 0.7 0.03 11.0 68.0 0.99218 3.3 0.41 9.8 5 0 white 95 5.2 0.185 0.22 1.0 0.03 47.0 123.0 0.99218 3.55 0.44 10.15 6 0 white 96 5.2 0.2 0.27 3.2 0.047 16.0 93.0 0.99235 3.44 0.53 10.1 7 1 white 97 5.2 0.21 0.31 1.7 0.048 17.0 61.0 0.98953 3.24 0.37 12.0 7 1 white 98 5.2 0.22 0.46 6.2 0.066 41.0 187.0 0.99362 3.19 0.42 9.73333333333333 5 0 white 99 5.2 0.24 0.15 7.1 0.043 32.0 134.0 0.99378 3.24 0.48 9.9 6 0 white 100 5.2 0.24 0.45 3.8 0.027 21.0 128.0 0.992 3.55 0.49 11.2 8 1 white Rows: 1-100 | Columns: 14Note
VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.
You can easily divide your dataset into training and testing subsets using the
vDataFrame.
train_test_split()
method. This is a crucial step when preparing your data for machine learning, as it allows you to evaluate the performance of your models accurately.data = vpd.load_winequality() train, test = data.train_test_split(test_size = 0.2)
Warning
In this case, VerticaPy utilizes seeded randomization to guarantee the reproducibility of your data split. However, please be aware that this approach may lead to reduced performance. For a more efficient data split, you can use the
vDataFrame.
to_db()
method to save your results intotables
ortemporary tables
. This will help enhance the overall performance of the process.Balancing the Dataset#
In VerticaPy, balancing a dataset to address class imbalances is made straightforward through the
balance()
function within thepreprocessing
module. This function enables users to rectify skewed class distributions efficiently. By specifying the target variable and setting parameters like the method for balancing, users can effortlessly achieve a more equitable representation of classes in their dataset. Whether opting for over-sampling, under-sampling, or a combination of both, VerticaPy’sbalance()
function streamlines the process, empowering users to enhance the performance and fairness of their machine learning models trained on imbalanced data.To balance the dataset, use the following syntax.
from verticapy.machine_learning.vertica.preprocessing import balance balanced_train = balance( name = "my_schema.train_balanced", input_relation = train, y = "good", method = "hybrid", )
Note
With this code, a table named train_balanced is created in the my_schema schema. It can then be used to train the model. In the rest of the example, we will work with the full dataset.
Hint
Balancing the dataset is a crucial step in improving the accuracy of machine learning models, particularly when faced with imbalanced class distributions. By addressing disparities in the number of instances across different classes, the model becomes more adept at learning patterns from all classes rather than being biased towards the majority class. This, in turn, enhances the model’s ability to make accurate predictions for under-represented classes. The balanced dataset ensures that the model is not dominated by the majority class and, as a result, leads to more robust and unbiased model performance. Therefore, by employing techniques such as over-sampling, under-sampling, or a combination of both during dataset preparation, practitioners can significantly contribute to achieving higher accuracy and better generalization of their machine learning models.
Model Initialization#
First we import the
DecisionTreeClassifier
model:from verticapy.machine_learning.vertica import DecisionTreeClassifier
Then we can create the model:
model = DecisionTreeClassifier( max_features = "auto", max_leaf_nodes = 32, max_depth = 3, min_samples_leaf = 5, min_info_gain = 0.0, nbins = 32, )
Hint
In
verticapy
1.0.x and higher, you do not need to specify the model name, as the name is automatically assigned. If you need to re-use the model, you can fetch the model name from the model’s attributes.Important
The model name is crucial for the model management system and versioning. It’s highly recommended to provide a name if you plan to reuse the model later.
Model Training#
We can now fit the model:
model.fit( train, [ "fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density" ], "good", test, )
Important
To train a model, you can directly use the
vDataFrame
or the name of the relation stored in the database. The test set is optional and is only used to compute the test metrics. Inverticapy
, we don’t work usingX
matrices andy
vectors. Instead, we work directly with lists of predictors and the response name.Features Importance#
We can conveniently get the features importance:
result = model.features_importance()
Note
In models such as
RandomForest
, feature importance is calculated using the MDI (Mean Decreased Impurity). To determine the final score, VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them.Metrics#
We can get the entire report using:
model.report()
value auc 0.6921140416510788 prc_auc 0.44367574607142607 accuracy 0.812933025404157 log_loss 0.193591346384012 precision 0.0 recall 0.0 f1_score 0.0 mcc 0.0 informedness 0.0 markedness -0.18706697459584298 csi 0.0 Rows: 1-11 | Columns: 2Important
Most metrics are computed using a single SQL query, but some of them might require multiple SQL queries. Selecting only the necessary metrics in the report can help optimize performance. E.g.
model.report(metrics = ["auc", "accuracy"])
.For classification models, we can easily modify the
cutoff
to observe the effect on different metrics:model.report(cutoff = 0.2)
value auc 0.6921140416510788 prc_auc 0.44367574607142607 accuracy 0.6712856043110085 log_loss 0.193591346384012 precision 0.29464285714285715 recall 0.5432098765432098 f1_score 0.38205499276411 mcc 0.2001535050687769 informedness 0.24396745230078576 markedness 0.16420807453416142 csi 0.23613595706618962 Rows: 1-11 | Columns: 2You can also use the
score()
function to compute any classification metric. The default metric is the accuracy:model.score() Out[3]: 0.812933025404157
Prediction#
Prediction is straight-forward:
model.predict( test, [ "fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density" ], "prediction", )
123fixed_acidityNumeric(8)123volatile_acidityNumeric(9)123citric_acidNumeric(8)123residual_sugarNumeric(9)123chloridesFloat(22)123free_sulfur_dioxideNumeric(9)123total_sulfur_dioxideNumeric(9)123densityFloat(22)123pHNumeric(8)123sulphatesNumeric(8)123alcoholFloat(22)123qualityInteger123goodIntegerAbccolorVarchar(20)AbcpredictionVarchar(1)1 3.8 0.31 0.02 11.1 0.036 20.0 114.0 0.99248 3.75 0.44 12.4 6 0 white 0 2 4.2 0.17 0.36 1.8 0.029 93.0 161.0 0.98999 3.65 0.89 12.0 7 1 white 0 3 4.2 0.215 0.23 5.1 0.041 64.0 157.0 0.99688 3.42 0.44 8.0 3 0 white 0 4 4.4 0.46 0.1 2.8 0.024 31.0 111.0 0.98816 3.48 0.34 13.1 6 0 white 0 5 4.4 0.54 0.09 5.1 0.038 52.0 97.0 0.99022 3.41 0.4 12.2 7 1 white 0 6 4.7 0.145 0.29 1.0 0.042 35.0 90.0 0.9908 3.76 0.49 11.3 6 0 white 0 7 4.7 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 10.5 5 0 white 0 8 4.8 0.13 0.32 1.2 0.042 40.0 98.0 0.9898 3.42 0.64 11.8 7 1 white 0 9 4.8 0.26 0.23 10.6 0.034 23.0 111.0 0.99274 3.46 0.28 11.5 7 1 white 0 10 4.9 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 10.4666666666667 5 0 white 0 11 4.9 0.42 0.0 2.1 0.048 16.0 42.0 0.99154 3.71 0.74 14.0 7 1 red 0 12 5.0 0.235 0.27 11.75 0.03 34.0 118.0 0.9954 3.07 0.5 9.4 6 0 white 0 13 5.0 0.3 0.33 3.7 0.03 54.0 173.0 0.9887 3.36 0.3 13.0 7 1 white 0 14 5.0 0.31 0.0 6.4 0.046 43.0 166.0 0.994 3.3 0.63 9.9 6 0 white 0 15 5.0 0.33 0.18 4.6 0.032 40.0 124.0 0.99114 3.18 0.4 11.0 6 0 white 0 16 5.0 0.35 0.25 7.8 0.031 24.0 116.0 0.99241 3.39 0.4 11.3 6 0 white 0 17 5.0 0.38 0.01 1.6 0.048 26.0 60.0 0.99084 3.7 0.75 14.0 6 0 red 0 18 5.1 0.23 0.18 1.0 0.053 13.0 99.0 0.98956 3.22 0.39 11.5 5 0 white 0 19 5.1 0.29 0.28 8.3 0.026 27.0 107.0 0.99308 3.36 0.37 11.0 6 0 white 0 20 5.1 0.31 0.3 0.9 0.037 28.0 152.0 0.992 3.54 0.56 10.1 6 0 white 0 21 5.1 0.33 0.22 1.6 0.027 18.0 89.0 0.9893 3.51 0.38 12.5 7 1 white 0 22 5.1 0.35 0.26 6.8 0.034 36.0 120.0 0.99188 3.38 0.4 11.5 6 0 white 0 23 5.1 0.39 0.21 1.7 0.027 15.0 72.0 0.9894 3.5 0.45 12.5 6 0 white 0 24 5.1 0.585 0.0 1.7 0.044 14.0 86.0 0.99264 3.56 0.94 12.9 7 1 red 0 25 5.2 0.155 0.33 1.6 0.028 13.0 59.0 0.98975 3.3 0.84 11.9 8 1 white 0 26 5.2 0.155 0.33 1.6 0.028 13.0 59.0 0.98975 3.3 0.84 11.9 8 1 white 0 27 5.2 0.16 0.34 0.8 0.029 26.0 77.0 0.99155 3.25 0.51 10.1 6 0 white 0 28 5.2 0.24 0.15 7.1 0.043 32.0 134.0 0.99378 3.24 0.48 9.9 6 0 white 0 29 5.2 0.285 0.29 5.15 0.035 64.0 138.0 0.9895 3.19 0.34 12.4 8 1 white 0 30 5.2 0.31 0.2 2.4 0.027 27.0 117.0 0.98886 3.56 0.45 13.0 7 1 white 0 31 5.2 0.645 0.0 2.15 0.08 15.0 28.0 0.99444 3.78 0.61 12.5 6 0 red 0 32 5.3 0.26 0.23 5.15 0.034 48.0 160.0 0.9952 3.82 0.51 10.5 7 1 white 0 33 5.3 0.275 0.24 7.4 0.038 28.0 114.0 0.99313 3.38 0.51 11.0 6 0 white 0 34 5.3 0.3 0.2 1.1 0.077 48.0 166.0 0.9944 3.3 0.54 8.7 4 0 white 0 35 5.3 0.32 0.12 6.6 0.043 22.0 141.0 0.9937 3.36 0.6 10.4 6 0 white 0 36 5.3 0.36 0.27 6.3 0.028 40.0 132.0 0.99186 3.37 0.4 11.6 6 0 white 0 37 5.3 0.47 0.11 2.2 0.048 16.0 89.0 0.99182 3.54 0.88 13.6 7 1 red 0 38 5.3 0.6 0.34 1.4 0.031 3.0 60.0 0.98854 3.27 0.38 13.0 6 0 white 0 39 5.3 0.76 0.03 2.7 0.043 27.0 93.0 0.9932 3.34 0.38 9.2 5 0 white 0 40 5.4 0.15 0.32 2.5 0.037 10.0 51.0 0.98878 3.04 0.58 12.6 6 0 white 0 41 5.4 0.205 0.16 12.55 0.051 31.0 115.0 0.99564 3.4 0.38 10.8 6 0 white 0 42 5.4 0.24 0.18 2.3 0.05 22.0 145.0 0.99207 3.24 0.46 10.3 5 0 white 0 43 5.4 0.29 0.38 1.2 0.029 31.0 132.0 0.98895 3.28 0.36 12.4 6 0 white 0 44 5.4 0.3 0.3 1.2 0.029 25.0 93.0 0.98742 3.31 0.4 13.6 7 1 white 0 45 5.4 0.42 0.27 2.0 0.092 23.0 55.0 0.99471 3.78 0.64 12.3 7 1 red 0 46 5.4 0.53 0.16 2.7 0.036 34.0 128.0 0.98856 3.2 0.53 13.2 8 1 white 0 47 5.4 0.595 0.1 2.8 0.042 26.0 80.0 0.9932 3.36 0.38 9.3 5 0 white 0 48 5.5 0.14 0.27 4.6 0.029 22.0 104.0 0.9949 3.34 0.44 9.0 5 0 white 0 49 5.5 0.15 0.32 14.0 0.031 16.0 99.0 0.99437 3.26 0.38 11.5 8 1 white 0 50 5.5 0.18 0.22 5.5 0.037 10.0 86.0 0.99156 3.46 0.44 12.2 5 0 white 0 51 5.5 0.28 0.21 1.6 0.032 23.0 85.0 0.99027 3.42 0.42 12.5 5 0 white 0 52 5.5 0.29 0.3 1.1 0.022 20.0 110.0 0.98869 3.34 0.38 12.8 7 1 white 0 53 5.5 0.315 0.38 2.6 0.033 10.0 69.0 0.9909 3.12 0.59 10.8 6 0 white 0 54 5.5 0.335 0.3 2.5 0.071 27.0 128.0 0.9924 3.14 0.51 9.6 6 0 white 0 55 5.5 0.34 0.26 2.2 0.021 31.0 119.0 0.98919 3.55 0.49 13.0 8 1 white 0 56 5.5 0.375 0.38 1.7 0.036 17.0 98.0 0.99142 3.29 0.39 10.5 6 0 white 0 57 5.5 0.42 0.09 1.6 0.019 18.0 68.0 0.9906 3.33 0.51 11.4 7 1 white 0 58 5.5 0.485 0.0 1.5 0.065 8.0 103.0 0.994 3.63 0.4 9.7 4 0 white 0 59 5.6 0.12 0.26 4.3 0.038 18.0 97.0 0.99477 3.36 0.46 9.2 5 0 white 0 60 5.6 0.13 0.27 4.8 0.028 22.0 104.0 0.9948 3.34 0.45 9.2 6 0 white 0 61 5.6 0.16 0.27 1.4 0.044 53.0 168.0 0.9918 3.28 0.37 10.1 6 0 white 0 62 5.6 0.19 0.47 4.5 0.03 19.0 112.0 0.9922 3.56 0.45 11.2 6 0 white 0 63 5.6 0.2 0.66 10.2 0.043 78.0 175.0 0.9945 2.98 0.43 10.4 7 1 white 0 64 5.6 0.21 0.24 4.4 0.027 37.0 150.0 0.991 3.3 0.31 11.5 7 1 white 0 65 5.6 0.23 0.29 3.1 0.023 19.0 89.0 0.99068 3.25 0.51 11.2 6 0 white 0 66 5.6 0.24 0.34 2.0 0.041 14.0 73.0 0.98981 3.04 0.45 11.6 7 1 white 0 67 5.6 0.26 0.5 11.4 0.029 25.0 93.0 0.99428 3.23 0.49 10.5 6 0 white 0 68 5.6 0.28 0.4 6.1 0.034 36.0 118.0 0.99144 3.21 0.43 12.1 7 1 white 0 69 5.6 0.33 0.28 1.2 0.031 33.0 97.0 0.99126 3.49 0.58 10.9 6 0 white 0 70 5.6 0.34 0.25 2.5 0.046 47.0 182.0 0.99093 3.21 0.4 11.3 5 0 white 0 71 5.6 0.35 0.14 5.0 0.046 48.0 198.0 0.9937 3.3 0.71 10.3 5 0 white 0 72 5.6 0.35 0.4 6.3 0.022 23.0 174.0 0.9922 3.54 0.5 11.6 7 1 white 0 73 5.6 0.615 0.0 1.6 0.089 16.0 59.0 0.9943 3.58 0.52 9.9 5 0 red 0 74 5.6 0.66 0.0 2.5 0.066 7.0 15.0 0.99256 3.52 0.58 12.9 5 0 red 0 75 5.7 0.1 0.27 1.3 0.047 21.0 100.0 0.9928 3.27 0.46 9.5 5 0 white 0 76 5.7 0.135 0.3 4.6 0.042 19.0 101.0 0.9946 3.31 0.42 9.3 6 0 white 0 77 5.7 0.15 0.28 3.7 0.045 57.0 151.0 0.9913 3.22 0.27 11.2 6 0 white 0 78 5.7 0.15 0.47 11.4 0.035 49.0 128.0 0.99456 3.03 0.34 10.5 8 1 white 0 79 5.7 0.16 0.26 6.3 0.043 28.0 113.0 0.9936 3.06 0.58 9.9 6 0 white 0 80 5.7 0.21 0.25 1.1 0.035 26.0 81.0 0.9902 3.31 0.52 11.4 6 0 white 0 81 5.7 0.21 0.32 0.9 0.038 38.0 121.0 0.99074 3.24 0.46 10.6 6 0 white 0 82 5.7 0.22 0.2 16.0 0.044 41.0 113.0 0.99862 3.22 0.46 8.9 6 0 white 0 83 5.7 0.22 0.2 16.0 0.044 41.0 113.0 0.99862 3.22 0.46 8.9 6 0 white 0 84 5.7 0.22 0.22 16.65 0.044 39.0 110.0 0.99855 3.24 0.48 9.0 6 0 white 0 85 5.7 0.22 0.22 16.65 0.044 39.0 110.0 0.99855 3.24 0.48 9.0 6 0 white 0 86 5.7 0.25 0.27 10.8 0.05 58.0 116.0 0.99592 3.1 0.5 9.8 6 0 white 0 87 5.7 0.25 0.32 12.2 0.041 43.0 127.0 0.99524 3.23 0.53 10.4 7 1 white 0 88 5.7 0.255 0.65 1.2 0.079 17.0 137.0 0.99307 3.2 0.42 9.4 5 0 white 0 89 5.7 0.26 0.25 10.4 0.02 7.0 57.0 0.994 3.39 0.37 10.6 5 0 white 0 90 5.7 0.26 0.27 4.1 0.201 73.5 189.5 0.9942 3.27 0.38 9.4 6 0 white 0 91 5.7 0.26 0.3 1.8 0.039 30.0 105.0 0.98995 3.48 0.52 12.5 7 1 white 0 92 5.7 0.265 0.28 6.9 0.036 46.0 150.0 0.99299 3.36 0.44 10.8 7 1 white 0 93 5.7 0.27 0.16 9.0 0.053 32.0 111.0 0.99474 3.36 0.37 10.4 6 0 white 0 94 5.7 0.28 0.36 1.8 0.041 38.0 90.0 0.99002 3.27 0.98 11.9 7 1 white 0 95 5.7 0.37 0.3 1.1 0.029 24.0 88.0 0.98883 3.18 0.39 11.7 6 0 white 0 96 5.7 0.45 0.42 1.1 0.051 61.0 197.0 0.9932 3.02 0.4 9.0 5 0 white 0 97 5.7 0.695 0.06 6.8 0.042 9.0 84.0 0.99432 3.44 0.44 10.2 5 0 white 0 98 5.8 0.15 0.32 1.2 0.037 14.0 119.0 0.99137 3.19 0.5 10.2 6 0 white 0 99 5.8 0.15 0.49 1.1 0.048 21.0 98.0 0.9929 3.19 0.48 9.2 5 0 white 0 100 5.8 0.17 0.34 1.8 0.045 96.0 170.0 0.99035 3.38 0.9 11.8 8 1 white 0 Rows: 1-100 | Columns: 15Note
Predictions can be made automatically using the test set, in which case you don’t need to specify the predictors. Alternatively, you can pass only the
vDataFrame
to thepredict()
function, but in this case, it’s essential that the column names of thevDataFrame
match the predictors and response name in the model.Probabilities#
It is also easy to get the model’s probabilities:
model.predict_proba( test, [ "fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density" ], "prediction", )
123fixed_acidityNumeric(8)123volatile_acidityNumeric(9)123citric_acidNumeric(8)123residual_sugarNumeric(9)123chloridesFloat(22)123free_sulfur_dioxideNumeric(9)123total_sulfur_dioxideNumeric(9)123densityFloat(22)123pHNumeric(8)123sulphatesNumeric(8)123alcoholFloat(22)123qualityInteger123goodIntegerAbccolorVarchar(20)AbcpredictionVarchar(1)Abcprediction_0Varchar(128)Abcprediction_1Varchar(128)1 3.8 0.31 0.02 11.1 0.036 20.0 114.0 0.99248 3.75 0.44 12.4 6 0 white 0 0.813072 0.186928 2 4.2 0.17 0.36 1.8 0.029 93.0 161.0 0.98999 3.65 0.89 12.0 7 1 white 0 0.553079 0.446921 3 4.2 0.215 0.23 5.1 0.041 64.0 157.0 0.99688 3.42 0.44 8.0 3 0 white 0 0.794501 0.205499 4 4.4 0.46 0.1 2.8 0.024 31.0 111.0 0.98816 3.48 0.34 13.1 6 0 white 0 0.553079 0.446921 5 4.4 0.54 0.09 5.1 0.038 52.0 97.0 0.99022 3.41 0.4 12.2 7 1 white 0 0.553079 0.446921 6 4.7 0.145 0.29 1.0 0.042 35.0 90.0 0.9908 3.76 0.49 11.3 6 0 white 0 0.553079 0.446921 7 4.7 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 10.5 5 0 white 0 0.794501 0.205499 8 4.8 0.13 0.32 1.2 0.042 40.0 98.0 0.9898 3.42 0.64 11.8 7 1 white 0 0.553079 0.446921 9 4.8 0.26 0.23 10.6 0.034 23.0 111.0 0.99274 3.46 0.28 11.5 7 1 white 0 0.813072 0.186928 10 4.9 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 10.4666666666667 5 0 white 0 0.794501 0.205499 11 4.9 0.42 0.0 2.1 0.048 16.0 42.0 0.99154 3.71 0.74 14.0 7 1 red 0 0.925926 0.0740741 12 5.0 0.235 0.27 11.75 0.03 34.0 118.0 0.9954 3.07 0.5 9.4 6 0 white 0 0.813072 0.186928 13 5.0 0.3 0.33 3.7 0.03 54.0 173.0 0.9887 3.36 0.3 13.0 7 1 white 0 0.553079 0.446921 14 5.0 0.31 0.0 6.4 0.046 43.0 166.0 0.994 3.3 0.63 9.9 6 0 white 0 0.794501 0.205499 15 5.0 0.33 0.18 4.6 0.032 40.0 124.0 0.99114 3.18 0.4 11.0 6 0 white 0 0.553079 0.446921 16 5.0 0.35 0.25 7.8 0.031 24.0 116.0 0.99241 3.39 0.4 11.3 6 0 white 0 0.813072 0.186928 17 5.0 0.38 0.01 1.6 0.048 26.0 60.0 0.99084 3.7 0.75 14.0 6 0 red 0 0.925926 0.0740741 18 5.1 0.23 0.18 1.0 0.053 13.0 99.0 0.98956 3.22 0.39 11.5 5 0 white 0 0.985386 0.0146138 19 5.1 0.29 0.28 8.3 0.026 27.0 107.0 0.99308 3.36 0.37 11.0 6 0 white 0 0.813072 0.186928 20 5.1 0.31 0.3 0.9 0.037 28.0 152.0 0.992 3.54 0.56 10.1 6 0 white 0 0.794501 0.205499 21 5.1 0.33 0.22 1.6 0.027 18.0 89.0 0.9893 3.51 0.38 12.5 7 1 white 0 0.553079 0.446921 22 5.1 0.35 0.26 6.8 0.034 36.0 120.0 0.99188 3.38 0.4 11.5 6 0 white 0 0.813072 0.186928 23 5.1 0.39 0.21 1.7 0.027 15.0 72.0 0.9894 3.5 0.45 12.5 6 0 white 0 0.553079 0.446921 24 5.1 0.585 0.0 1.7 0.044 14.0 86.0 0.99264 3.56 0.94 12.9 7 1 red 0 0.794501 0.205499 25 5.2 0.155 0.33 1.6 0.028 13.0 59.0 0.98975 3.3 0.84 11.9 8 1 white 0 0.553079 0.446921 26 5.2 0.155 0.33 1.6 0.028 13.0 59.0 0.98975 3.3 0.84 11.9 8 1 white 0 0.553079 0.446921 27 5.2 0.16 0.34 0.8 0.029 26.0 77.0 0.99155 3.25 0.51 10.1 6 0 white 0 0.553079 0.446921 28 5.2 0.24 0.15 7.1 0.043 32.0 134.0 0.99378 3.24 0.48 9.9 6 0 white 0 0.813072 0.186928 29 5.2 0.285 0.29 5.15 0.035 64.0 138.0 0.9895 3.19 0.34 12.4 8 1 white 0 0.553079 0.446921 30 5.2 0.31 0.2 2.4 0.027 27.0 117.0 0.98886 3.56 0.45 13.0 7 1 white 0 0.553079 0.446921 31 5.2 0.645 0.0 2.15 0.08 15.0 28.0 0.99444 3.78 0.61 12.5 6 0 red 0 0.925926 0.0740741 32 5.3 0.26 0.23 5.15 0.034 48.0 160.0 0.9952 3.82 0.51 10.5 7 1 white 0 0.794501 0.205499 33 5.3 0.275 0.24 7.4 0.038 28.0 114.0 0.99313 3.38 0.51 11.0 6 0 white 0 0.813072 0.186928 34 5.3 0.3 0.2 1.1 0.077 48.0 166.0 0.9944 3.3 0.54 8.7 4 0 white 0 0.985386 0.0146138 35 5.3 0.32 0.12 6.6 0.043 22.0 141.0 0.9937 3.36 0.6 10.4 6 0 white 0 0.794501 0.205499 36 5.3 0.36 0.27 6.3 0.028 40.0 132.0 0.99186 3.37 0.4 11.6 6 0 white 0 0.553079 0.446921 37 5.3 0.47 0.11 2.2 0.048 16.0 89.0 0.99182 3.54 0.88 13.6 7 1 red 0 0.925926 0.0740741 38 5.3 0.6 0.34 1.4 0.031 3.0 60.0 0.98854 3.27 0.38 13.0 6 0 white 0 0.553079 0.446921 39 5.3 0.76 0.03 2.7 0.043 27.0 93.0 0.9932 3.34 0.38 9.2 5 0 white 0 0.794501 0.205499 40 5.4 0.15 0.32 2.5 0.037 10.0 51.0 0.98878 3.04 0.58 12.6 6 0 white 0 0.553079 0.446921 41 5.4 0.205 0.16 12.55 0.051 31.0 115.0 0.99564 3.4 0.38 10.8 6 0 white 0 0.985386 0.0146138 42 5.4 0.24 0.18 2.3 0.05 22.0 145.0 0.99207 3.24 0.46 10.3 5 0 white 0 0.985386 0.0146138 43 5.4 0.29 0.38 1.2 0.029 31.0 132.0 0.98895 3.28 0.36 12.4 6 0 white 0 0.553079 0.446921 44 5.4 0.3 0.3 1.2 0.029 25.0 93.0 0.98742 3.31 0.4 13.6 7 1 white 0 0.553079 0.446921 45 5.4 0.42 0.27 2.0 0.092 23.0 55.0 0.99471 3.78 0.64 12.3 7 1 red 0 0.855271 0.144729 46 5.4 0.53 0.16 2.7 0.036 34.0 128.0 0.98856 3.2 0.53 13.2 8 1 white 0 0.553079 0.446921 47 5.4 0.595 0.1 2.8 0.042 26.0 80.0 0.9932 3.36 0.38 9.3 5 0 white 0 0.794501 0.205499 48 5.5 0.14 0.27 4.6 0.029 22.0 104.0 0.9949 3.34 0.44 9.0 5 0 white 0 0.794501 0.205499 49 5.5 0.15 0.32 14.0 0.031 16.0 99.0 0.99437 3.26 0.38 11.5 8 1 white 0 0.813072 0.186928 50 5.5 0.18 0.22 5.5 0.037 10.0 86.0 0.99156 3.46 0.44 12.2 5 0 white 0 0.553079 0.446921 51 5.5 0.28 0.21 1.6 0.032 23.0 85.0 0.99027 3.42 0.42 12.5 5 0 white 0 0.553079 0.446921 52 5.5 0.29 0.3 1.1 0.022 20.0 110.0 0.98869 3.34 0.38 12.8 7 1 white 0 0.553079 0.446921 53 5.5 0.315 0.38 2.6 0.033 10.0 69.0 0.9909 3.12 0.59 10.8 6 0 white 0 0.553079 0.446921 54 5.5 0.335 0.3 2.5 0.071 27.0 128.0 0.9924 3.14 0.51 9.6 6 0 white 0 0.855271 0.144729 55 5.5 0.34 0.26 2.2 0.021 31.0 119.0 0.98919 3.55 0.49 13.0 8 1 white 0 0.553079 0.446921 56 5.5 0.375 0.38 1.7 0.036 17.0 98.0 0.99142 3.29 0.39 10.5 6 0 white 0 0.553079 0.446921 57 5.5 0.42 0.09 1.6 0.019 18.0 68.0 0.9906 3.33 0.51 11.4 7 1 white 0 0.553079 0.446921 58 5.5 0.485 0.0 1.5 0.065 8.0 103.0 0.994 3.63 0.4 9.7 4 0 white 0 0.925926 0.0740741 59 5.6 0.12 0.26 4.3 0.038 18.0 97.0 0.99477 3.36 0.46 9.2 5 0 white 0 0.794501 0.205499 60 5.6 0.13 0.27 4.8 0.028 22.0 104.0 0.9948 3.34 0.45 9.2 6 0 white 0 0.794501 0.205499 61 5.6 0.16 0.27 1.4 0.044 53.0 168.0 0.9918 3.28 0.37 10.1 6 0 white 0 0.553079 0.446921 62 5.6 0.19 0.47 4.5 0.03 19.0 112.0 0.9922 3.56 0.45 11.2 6 0 white 0 0.794501 0.205499 63 5.6 0.2 0.66 10.2 0.043 78.0 175.0 0.9945 2.98 0.43 10.4 7 1 white 0 0.989899 0.010101 64 5.6 0.21 0.24 4.4 0.027 37.0 150.0 0.991 3.3 0.31 11.5 7 1 white 0 0.553079 0.446921 65 5.6 0.23 0.29 3.1 0.023 19.0 89.0 0.99068 3.25 0.51 11.2 6 0 white 0 0.553079 0.446921 66 5.6 0.24 0.34 2.0 0.041 14.0 73.0 0.98981 3.04 0.45 11.6 7 1 white 0 0.553079 0.446921 67 5.6 0.26 0.5 11.4 0.029 25.0 93.0 0.99428 3.23 0.49 10.5 6 0 white 0 0.989899 0.010101 68 5.6 0.28 0.4 6.1 0.034 36.0 118.0 0.99144 3.21 0.43 12.1 7 1 white 0 0.553079 0.446921 69 5.6 0.33 0.28 1.2 0.031 33.0 97.0 0.99126 3.49 0.58 10.9 6 0 white 0 0.553079 0.446921 70 5.6 0.34 0.25 2.5 0.046 47.0 182.0 0.99093 3.21 0.4 11.3 5 0 white 0 0.553079 0.446921 71 5.6 0.35 0.14 5.0 0.046 48.0 198.0 0.9937 3.3 0.71 10.3 5 0 white 0 0.794501 0.205499 72 5.6 0.35 0.4 6.3 0.022 23.0 174.0 0.9922 3.54 0.5 11.6 7 1 white 0 0.794501 0.205499 73 5.6 0.615 0.0 1.6 0.089 16.0 59.0 0.9943 3.58 0.52 9.9 5 0 red 0 0.925926 0.0740741 74 5.6 0.66 0.0 2.5 0.066 7.0 15.0 0.99256 3.52 0.58 12.9 5 0 red 0 0.925926 0.0740741 75 5.7 0.1 0.27 1.3 0.047 21.0 100.0 0.9928 3.27 0.46 9.5 5 0 white 0 0.65942 0.34058 76 5.7 0.135 0.3 4.6 0.042 19.0 101.0 0.9946 3.31 0.42 9.3 6 0 white 0 0.794501 0.205499 77 5.7 0.15 0.28 3.7 0.045 57.0 151.0 0.9913 3.22 0.27 11.2 6 0 white 0 0.553079 0.446921 78 5.7 0.15 0.47 11.4 0.035 49.0 128.0 0.99456 3.03 0.34 10.5 8 1 white 0 0.813072 0.186928 79 5.7 0.16 0.26 6.3 0.043 28.0 113.0 0.9936 3.06 0.58 9.9 6 0 white 0 0.794501 0.205499 80 5.7 0.21 0.25 1.1 0.035 26.0 81.0 0.9902 3.31 0.52 11.4 6 0 white 0 0.553079 0.446921 81 5.7 0.21 0.32 0.9 0.038 38.0 121.0 0.99074 3.24 0.46 10.6 6 0 white 0 0.553079 0.446921 82 5.7 0.22 0.2 16.0 0.044 41.0 113.0 0.99862 3.22 0.46 8.9 6 0 white 0 0.813072 0.186928 83 5.7 0.22 0.2 16.0 0.044 41.0 113.0 0.99862 3.22 0.46 8.9 6 0 white 0 0.813072 0.186928 84 5.7 0.22 0.22 16.65 0.044 39.0 110.0 0.99855 3.24 0.48 9.0 6 0 white 0 0.813072 0.186928 85 5.7 0.22 0.22 16.65 0.044 39.0 110.0 0.99855 3.24 0.48 9.0 6 0 white 0 0.813072 0.186928 86 5.7 0.25 0.27 10.8 0.05 58.0 116.0 0.99592 3.1 0.5 9.8 6 0 white 0 0.855271 0.144729 87 5.7 0.25 0.32 12.2 0.041 43.0 127.0 0.99524 3.23 0.53 10.4 7 1 white 0 0.813072 0.186928 88 5.7 0.255 0.65 1.2 0.079 17.0 137.0 0.99307 3.2 0.42 9.4 5 0 white 0 0.855271 0.144729 89 5.7 0.26 0.25 10.4 0.02 7.0 57.0 0.994 3.39 0.37 10.6 5 0 white 0 0.813072 0.186928 90 5.7 0.26 0.27 4.1 0.201 73.5 189.5 0.9942 3.27 0.38 9.4 6 0 white 0 0.855271 0.144729 91 5.7 0.26 0.3 1.8 0.039 30.0 105.0 0.98995 3.48 0.52 12.5 7 1 white 0 0.553079 0.446921 92 5.7 0.265 0.28 6.9 0.036 46.0 150.0 0.99299 3.36 0.44 10.8 7 1 white 0 0.813072 0.186928 93 5.7 0.27 0.16 9.0 0.053 32.0 111.0 0.99474 3.36 0.37 10.4 6 0 white 0 0.985386 0.0146138 94 5.7 0.28 0.36 1.8 0.041 38.0 90.0 0.99002 3.27 0.98 11.9 7 1 white 0 0.553079 0.446921 95 5.7 0.37 0.3 1.1 0.029 24.0 88.0 0.98883 3.18 0.39 11.7 6 0 white 0 0.553079 0.446921 96 5.7 0.45 0.42 1.1 0.051 61.0 197.0 0.9932 3.02 0.4 9.0 5 0 white 0 0.855271 0.144729 97 5.7 0.695 0.06 6.8 0.042 9.0 84.0 0.99432 3.44 0.44 10.2 5 0 white 0 0.813072 0.186928 98 5.8 0.15 0.32 1.2 0.037 14.0 119.0 0.99137 3.19 0.5 10.2 6 0 white 0 0.553079 0.446921 99 5.8 0.15 0.49 1.1 0.048 21.0 98.0 0.9929 3.19 0.48 9.2 5 0 white 0 0.65942 0.34058 100 5.8 0.17 0.34 1.8 0.045 96.0 170.0 0.99035 3.38 0.9 11.8 8 1 white 0 0.553079 0.446921 Rows: 1-100 | Columns: 17Note
Probabilities are added to the
vDataFrame
, and VerticaPy uses the corresponding probability function in SQL behind the scenes. You can use thepos_label
parameter to add only the probability of the selected category.Confusion Matrix#
You can obtain the confusion matrix of your choice by specifying the desired cutoff.
model.confusion_matrix(cutoff = 0.5) Out[4]: array([[1056, 0], [ 243, 0]])
Note
In classification, the
cutoff
is a threshold value used to determine class assignment based on predicted probabilities or scores from a classification model. In binary classification, if the predicted probability for a specific class is greater than or equal to the cutoff, the instance is assigned to the positive class; otherwise, it is assigned to the negative class. Adjusting the cutoff allows for trade-offs between true positives and false positives, enabling the model to be optimized for specific objectives or to consider the relative costs of different classification errors. The choice of cutoff is critical for tailoring the model’s performance to meet specific needs.Main Plots (Classification Curves)#
Classification models allow for the creation of various plots that are very helpful in understanding the model, such as the ROC Curve, PRC Curve, Cutoff Curve, Gain Curve, and more.
Most of the classification curves can be found in the Machine Learning - Classification Curve.
For example, let’s draw the model’s ROC curve.
model.roc_curve()
Important
Most of the curves have a parameter called
nbins
, which is essential for estimating metrics. The larger thenbins
, the more precise the estimation, but it can significantly impact performance. Exercise caution when increasing this parameter excessively.Hint
In binary classification, various curves can be easily plotted. However, in multi-class classification, it’s important to select the
pos_label
, representing the class to be treated as positive when drawing the curve.Other Plots#
Tree models can be visualized by drawing their tree plots. For more examples, check out Machine Learning - Tree Plots.
model.plot_tree()
Note
The above example may not render properly in the doc because of the huge size of the tree. But it should render nicely in jupyter environment.
In order to plot graph using graphviz separately, you can extract the graphviz DOT file code as follows:
model.to_graphviz() Out[5]: 'digraph Tree {\ngraph [bgcolor="#FFFFFF00"];\n0 [label="\\"chlorides\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n0 -> 1 [label="<= 0.046625", color="#666666", fontcolor="#666666"]\n0 -> 2 [label="> 0.046625", color="#666666", fontcolor="#666666"]\n1 [label="\\"residual_sugar\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n1 -> 3 [label="<= 6.7125", color="#666666", fontcolor="#666666"]\n1 -> 4 [label="> 6.7125", color="#666666", fontcolor="#666666"]\n2 [label="\\"citric_acid\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n2 -> 5 [label="<= 0.25", color="#666666", fontcolor="#666666"]\n2 -> 6 [label="> 0.25", color="#666666", fontcolor="#666666"]\n3 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n3 -> 7 [label="<= 0.991973", color="#666666", fontcolor="#666666"]\n3 -> 8 [label="> 0.991973", color="#666666", fontcolor="#666666"]\n4 [label="\\"citric_acid\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n4 -> 9 [label="<= 0.5", color="#666666", fontcolor="#666666"]\n4 -> 10 [label="> 0.5", color="#666666", fontcolor="#666666"]\n5 [label="\\"citric_acid\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n5 -> 11 [label="<= 0.125", color="#666666", fontcolor="#666666"]\n5 -> 12 [label="> 0.125", color="#666666", fontcolor="#666666"]\n6 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n6 -> 13 [label="<= 0.17375", color="#666666", fontcolor="#666666"]\n6 -> 14 [label="> 0.17375", color="#666666", fontcolor="#666666"]\n7 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.55</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.45</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n8 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.79</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.21</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n9 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.81</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.19</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n10 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.99</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.01</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n11 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.93</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.07</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n12 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.99</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.01</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n13 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.66</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.34</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n14 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.86</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.14</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n}'
This string can then be copied into a DOT file which can beparsed by graphviz.
Contour plot is another useful plot that can be produced for models with two predictors.
model.contour()
Important
Machine learning models with two predictors can usually benefit from their own contour plot. This visual representation aids in exploring predictions and gaining a deeper understanding of how these models perform in different scenarios. Please refer to Contour Plot for more examples.
Parameter Modification#
In order to see the parameters:
model.get_params() Out[6]: {'max_features': 'auto', 'max_leaf_nodes': 32, 'max_depth': 3, 'min_samples_leaf': 5, 'min_info_gain': 0.0, 'nbins': 32}
And to manually change some of the parameters:
model.set_params({'max_depth': 5})
Model Register#
In order to register the model for tracking and versioning:
model.register("model_v1")
Please refer to Model Tracking and Versioning for more details on model tracking and versioning.
Model Exporting#
To Memmodel
model.to_memmodel()
Note
MemModel
objects serve as in-memory representations of machine learning models. They can be used for both in-database and in-memory prediction tasks. These objects can be pickled in the same way that you would pickle ascikit-learn
model.The following methods for exporting the model use
MemModel
, and it is recommended to useMemModel
directly.To SQL
You can get the SQL code by:
model.to_sql() Out[8]: '(CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "residual_sugar" < 6.7125 THEN (CASE WHEN "density" < 0.991973 THEN 0 ELSE 0 END) ELSE (CASE WHEN "citric_acid" < 0.5 THEN 0 ELSE 0 END) END) ELSE (CASE WHEN "citric_acid" < 0.25 THEN (CASE WHEN "citric_acid" < 0.125 THEN 0 ELSE 0 END) ELSE (CASE WHEN "volatile_acidity" < 0.17375 THEN 0 ELSE 0 END) END) END)'
To Python
To obtain the prediction function in Python syntax, use the following code:
X = [[4.2, 0.17, 0.36, 1.8, 0.029, 0.9899]] model.to_python()(X) Out[10]: array([0])
Hint
The
to_python()
method is used to retrieve predictions, probabilities, or cluster distances. For specific details on how to use this method for different model types, refer to the relevant documentation for each model.- __init__(name: str = None, overwrite_model: bool = False, max_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: int | float | Decimal = 1000000000.0, max_depth: int = 100, min_samples_leaf: int = 1, min_info_gain: int | float | Decimal = 0.0, nbins: int = 32) None #
Must be overridden in the child class
Methods
__init__
([name, overwrite_model, ...])Must be overridden in the child class
classification_report
([metrics, cutoff, ...])Computes a classification report using multiple model evaluation metrics (
auc
,accuracy
,f1
...).confusion_matrix
([pos_label, cutoff])Computes the model confusion matrix.
contour
([pos_label, nbins, chart])Draws the model's contour plot.
cutoff_curve
([pos_label, nbins, show, chart])Draws the model Cutoff curve.
deploySQL
([X, pos_label, cutoff, allSQL])Returns the SQL code needed to deploy the model.
does_model_exists
(name[, raise_error, ...])Checks whether the model is stored in the Vertica database.
drop
()Drops the model from the Vertica database.
export_models
(name, path[, kind])Exports machine learning models.
features_importance
([tree_id, show, chart])Computes the model's features importance.
fit
(input_relation, X, y[, test_relation, ...])Trains the model.
get_attributes
([attr_name])Returns the model attributes.
get_match_index
(x, col_list[, str_check])Returns the matching index.
Returns the parameters of the model.
get_plotting_lib
([class_name, chart, ...])Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
get_score
([tree_id])Returns the feature importance metrics for the input tree.
get_tree
([tree_id])Returns a table with all the input tree information.
get_vertica_attributes
([attr_name])Returns the model Vertica attributes.
import_models
(path[, schema, kind])Imports machine learning models.
lift_chart
([pos_label, nbins, show, chart])Draws the model Lift Chart.
plot
([max_nb_points, chart])Draws the model.
plot_tree
([tree_id, pic_path])Draws the input tree.
prc_curve
([pos_label, nbins, show, chart])Draws the model PRC curve.
predict
(vdf[, X, name, cutoff, inplace])Predicts using the input relation.
predict_proba
(vdf[, X, name, pos_label, inplace])Returns the model's probabilities using the input relation.
register
(registered_name[, raise_error])Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
report
([metrics, cutoff, labels, nbins])Computes a classification report using multiple model evaluation metrics (
auc
,accuracy
,f1
...).roc_curve
([pos_label, nbins, show, chart])Draws the model ROC curve.
score
([metric, average, pos_label, cutoff, ...])Computes the model score.
set_params
([parameters])Sets the parameters of the model.
Summarizes the model.
to_binary
(path)Exports the model to the Vertica Binary format.
to_graphviz
([tree_id, classes_color, ...])Returns the code for a Graphviz tree.
Converts the model to an InMemory object that can be used for different types of predictions.
to_pmml
(path)Exports the model to PMML.
to_python
([return_proba, ...])Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
to_sql
([X, return_proba, ...])Returns the SQL code needed to deploy the model without using built-in Vertica functions.
to_tf
(path)Exports the model to the Frozen Graph format (TensorFlow).
Attributes