verticapy.machine_learning.vertica.ensemble.RandomForestClassifier.predict#
- RandomForestClassifier.predict(vdf: str | vDataFrame, X: str | list[str] | None = None, name: str | None = None, cutoff: int | float | Decimal | None = None, inplace: bool = True) vDataFrame #
Predicts using the input relation.
Parameters#
- vdf: SQLRelation
Object used to run the prediction. You can also specify a customized relation, but you must enclose it with an alias. For example,
(SELECT 1) x
is valid, whereas(SELECT 1)
and “SELECT 1” are invalid.- X: SQLColumns, optional
List of the columns used to deploy the models. If empty, the model predictors are used.
- name: str, optional
Name of the added vDataColumn. If empty, a name is generated.
- cutoff: PythonNumber, optional
Cutoff for which the tested category is accepted as a prediction. This parameter is only used for binary classification.
- inplace: bool, optional
If set to True, the prediction is added to the vDataFrame.
Returns#
- vDataFrame
the input object.
Examples#
For this example, we will use the Iris dataset.
import verticapy.datasets as vpd data = vpd.load_iris() train, test = data.train_test_split(test_size = 0.2)
123SepalLengthCmNumeric(7)123SepalWidthCmNumeric(7)123PetalLengthCmNumeric(7)123PetalWidthCmNumeric(7)AbcSpeciesVarchar(30)1 3.3 4.5 5.6 7.8 Iris-setosa 2 3.3 4.5 5.6 7.8 Iris-setosa 3 3.3 4.5 5.6 7.8 Iris-setosa 4 3.3 4.5 5.6 7.8 Iris-setosa 5 3.3 4.5 5.6 7.8 Iris-setosa 6 3.3 4.5 5.6 7.8 Iris-setosa 7 3.3 4.5 5.6 7.8 Iris-setosa 8 3.3 4.5 5.6 7.8 Iris-setosa 9 3.3 4.5 5.6 7.8 Iris-setosa 10 3.3 4.5 5.6 7.8 Iris-setosa 11 3.3 4.5 5.6 7.8 Iris-setosa 12 3.3 4.5 5.6 7.8 Iris-setosa 13 3.3 4.5 5.6 7.8 Iris-setosa 14 3.3 4.5 5.6 7.8 Iris-setosa 15 3.3 4.5 5.6 7.8 Iris-setosa 16 3.3 4.5 5.6 7.8 Iris-setosa 17 3.3 4.5 5.6 7.8 Iris-setosa 18 3.3 4.5 5.6 7.8 Iris-setosa 19 3.3 4.5 5.6 7.8 Iris-setosa 20 3.3 4.5 5.6 7.8 Iris-setosa 21 3.3 4.5 5.6 7.8 Iris-setosa 22 3.3 4.5 5.6 7.8 Iris-setosa 23 3.3 4.5 5.6 7.8 Iris-setosa 24 3.3 4.5 5.6 7.8 Iris-setosa 25 3.3 4.5 5.6 7.8 Iris-setosa 26 3.3 4.5 5.6 7.8 Iris-setosa 27 3.3 4.5 5.6 7.8 Iris-setosa 28 3.3 4.5 5.6 7.8 Iris-setosa 29 3.3 4.5 5.6 7.8 Iris-setosa 30 3.3 4.5 5.6 7.8 Iris-setosa 31 3.3 4.5 5.6 7.8 Iris-setosa 32 3.3 4.5 5.6 7.8 Iris-setosa 33 3.3 4.5 5.6 7.8 Iris-setosa 34 3.3 4.5 5.6 7.8 Iris-setosa 35 3.3 4.5 5.6 7.8 Iris-setosa 36 3.3 4.5 5.6 7.8 Iris-setosa 37 3.3 4.5 5.6 7.8 Iris-setosa 38 3.3 4.5 5.6 7.8 Iris-setosa 39 3.3 4.5 5.6 7.8 Iris-setosa 40 3.3 4.5 5.6 7.8 Iris-setosa 41 3.3 4.5 5.6 7.8 Iris-setosa 42 3.3 4.5 5.6 7.8 Iris-setosa 43 4.3 3.0 1.1 0.1 Iris-setosa 44 4.3 4.7 9.6 1.8 Iris-virginica 45 4.3 4.7 9.6 1.8 Iris-virginica 46 4.3 4.7 9.6 1.8 Iris-virginica 47 4.3 4.7 9.6 1.8 Iris-virginica 48 4.3 4.7 9.6 1.8 Iris-virginica 49 4.3 4.7 9.6 1.8 Iris-virginica 50 4.3 4.7 9.6 1.8 Iris-virginica 51 4.3 4.7 9.6 1.8 Iris-virginica 52 4.3 4.7 9.6 1.8 Iris-virginica 53 4.3 4.7 9.6 1.8 Iris-virginica 54 4.3 4.7 9.6 1.8 Iris-virginica 55 4.3 4.7 9.6 1.8 Iris-virginica 56 4.3 4.7 9.6 1.8 Iris-virginica 57 4.3 4.7 9.6 1.8 Iris-virginica 58 4.3 4.7 9.6 1.8 Iris-virginica 59 4.3 4.7 9.6 1.8 Iris-virginica 60 4.3 4.7 9.6 1.8 Iris-virginica 61 4.3 4.7 9.6 1.8 Iris-virginica 62 4.3 4.7 9.6 1.8 Iris-virginica 63 4.3 4.7 9.6 1.8 Iris-virginica 64 4.3 4.7 9.6 1.8 Iris-virginica 65 4.3 4.7 9.6 1.8 Iris-virginica 66 4.3 4.7 9.6 1.8 Iris-virginica 67 4.3 4.7 9.6 1.8 Iris-virginica 68 4.3 4.7 9.6 1.8 Iris-virginica 69 4.3 4.7 9.6 1.8 Iris-virginica 70 4.3 4.7 9.6 1.8 Iris-virginica 71 4.3 4.7 9.6 1.8 Iris-virginica 72 4.3 4.7 9.6 1.8 Iris-virginica 73 4.3 4.7 9.6 1.8 Iris-virginica 74 4.3 4.7 9.6 1.8 Iris-virginica 75 4.3 4.7 9.6 1.8 Iris-virginica 76 4.3 4.7 9.6 1.8 Iris-virginica 77 4.3 4.7 9.6 1.8 Iris-virginica 78 4.3 4.7 9.6 1.8 Iris-virginica 79 4.3 4.7 9.6 1.8 Iris-virginica 80 4.3 4.7 9.6 1.8 Iris-virginica 81 4.3 4.7 9.6 1.8 Iris-virginica 82 4.3 4.7 9.6 1.8 Iris-virginica 83 4.3 4.7 9.6 1.8 Iris-virginica 84 4.3 4.7 9.6 1.8 Iris-virginica 85 4.3 4.7 9.6 1.8 Iris-virginica 86 4.4 2.9 1.4 0.2 Iris-setosa 87 4.4 3.0 1.3 0.2 Iris-setosa 88 4.4 3.2 1.3 0.2 Iris-setosa 89 4.5 2.3 1.3 0.3 Iris-setosa 90 4.6 3.1 1.5 0.2 Iris-setosa 91 4.6 3.2 1.4 0.2 Iris-setosa 92 4.6 3.4 1.4 0.3 Iris-setosa 93 4.6 3.6 1.0 0.2 Iris-setosa 94 4.7 3.2 1.3 0.2 Iris-setosa 95 4.7 3.2 1.6 0.2 Iris-setosa 96 4.8 3.0 1.4 0.1 Iris-setosa 97 4.8 3.0 1.4 0.3 Iris-setosa 98 4.8 3.1 1.6 0.2 Iris-setosa 99 4.8 3.4 1.6 0.2 Iris-setosa 100 4.8 3.4 1.9 0.2 Iris-setosa Rows: 1-100 | Columns: 5Let’s import the model:
from verticapy.machine_learning.vertica import NearestCentroid
Then we can create the model:
model = NearestCentroid(p = 2)
We can now fit the model:
model.fit( train, [ "SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm", ], "Species", test, )
We can then get the prediction:
model.predict(test, name = "prediction"
123SepalLengthCmNumeric(7)123SepalWidthCmNumeric(7)123PetalLengthCmNumeric(7)123PetalWidthCmNumeric(7)AbcSpeciesVarchar(30)AbcpredictionVarchar(15)1 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 2 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 3 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 4 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 5 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 6 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 7 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 8 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 9 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 10 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 11 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 12 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 13 4.3 4.7 9.6 1.8 Iris-virginica Iris-virginica 14 4.3 4.7 9.6 1.8 Iris-virginica Iris-virginica 15 4.3 4.7 9.6 1.8 Iris-virginica Iris-virginica 16 4.3 4.7 9.6 1.8 Iris-virginica Iris-virginica 17 4.3 4.7 9.6 1.8 Iris-virginica Iris-virginica 18 4.4 2.9 1.4 0.2 Iris-setosa Iris-versicolor 19 4.4 3.0 1.3 0.2 Iris-setosa Iris-versicolor 20 4.6 3.6 1.0 0.2 Iris-setosa Iris-versicolor 21 4.9 2.4 3.3 1.0 Iris-versicolor Iris-versicolor 22 4.9 3.1 1.5 0.1 Iris-setosa Iris-versicolor 23 5.0 2.3 3.3 1.0 Iris-versicolor Iris-versicolor 24 5.0 3.4 1.5 0.2 Iris-setosa Iris-versicolor 25 5.1 2.5 3.0 1.1 Iris-versicolor Iris-versicolor 26 5.1 3.8 1.6 0.2 Iris-setosa Iris-versicolor 27 5.2 4.1 1.5 0.1 Iris-setosa Iris-versicolor 28 5.3 3.7 1.5 0.2 Iris-setosa Iris-versicolor 29 5.4 3.9 1.7 0.4 Iris-setosa Iris-versicolor 30 5.5 4.2 1.4 0.2 Iris-setosa Iris-versicolor 31 5.6 2.8 4.9 2.0 Iris-virginica Iris-versicolor 32 5.6 2.9 3.6 1.3 Iris-versicolor Iris-versicolor 33 5.7 2.8 4.5 1.3 Iris-versicolor Iris-versicolor 34 6.1 2.6 5.6 1.4 Iris-virginica Iris-versicolor 35 6.1 2.8 4.7 1.2 Iris-versicolor Iris-versicolor 36 6.2 2.8 4.8 1.8 Iris-virginica Iris-versicolor 37 6.4 2.7 5.3 1.9 Iris-virginica Iris-versicolor 38 6.4 2.9 4.3 1.3 Iris-versicolor Iris-versicolor 39 6.4 3.1 5.5 1.8 Iris-virginica Iris-versicolor 40 6.4 3.2 5.3 2.3 Iris-virginica Iris-versicolor 41 6.5 3.0 5.2 2.0 Iris-virginica Iris-versicolor 42 6.6 2.9 4.6 1.3 Iris-versicolor Iris-versicolor 43 6.6 3.0 4.4 1.4 Iris-versicolor Iris-versicolor 44 6.7 3.0 5.0 1.7 Iris-versicolor Iris-versicolor 45 7.4 2.8 6.1 1.9 Iris-virginica Iris-versicolor 46 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 47 3.3 4.5 5.6 7.8 Iris-setosa Iris-setosa 48 4.3 4.7 9.6 1.8 Iris-virginica Iris-virginica 49 4.3 4.7 9.6 1.8 Iris-virginica Iris-virginica Rows: 1-49 | Columns: 6Important
For this example, a specific model is utilized, and it may not correspond exactly to the model you are working with. To see a comprehensive example specific to your class of interest, please refer to that particular class.