verticapy.machine_learning.vertica.ensemble.IsolationForest.decision_function¶
- IsolationForest.decision_function(vdf: Annotated[str | vDataFrame, ''], X: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, name: str | None = None, inplace: bool = True) vDataFrame¶
Returns the anomaly score using the input relation.
Parameters¶
- vdf: SQLRelation
Object to use for the prediction. You can specify a customized relation if it is enclosed with an alias. For example,``(SELECT 1) x`` is valid, whereas
(SELECT 1)andSELECT 1are invalid.- X: SQLColumns, optional
listof columns used to deploy the models. If empty, the model predictors are used.- name: str, optional
Name of the additional
vDataColumn. If empty, a name is generated.- inplace: bool, optional
If
True, the prediction is added to thevDataFrame.
Returns¶
- vDataFrame
the input object.
Examples¶
We import
verticapy:import verticapy as vp
For this example, we will use the winequality dataset.
import verticapy.datasets as vpd data = vpd.load_winequality()
123fixed_acidity123volatile_acidity123citric_acid123residual_sugar123chlorides123free_sulfur_dioxide123total_sulfur_dioxide123density123pH123sulphates123alcohol123quality123goodAbccolor1 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: 14We import the
IsolationForestmodel:from verticapy.machine_learning.vertica import IsolationForest
Then we can create the model:
model = IsolationForest( n_estimators = 10, max_depth = 3, nbins = 6, )
We can now fit the model:
model.fit(data, X = ["density", "sulphates"]) =========== call_string =========== SELECT iforest('"public"."_verticapy_tmp_isolationforest_v_demo_b303e08855a311ef880f0242ac120002_"', '"public"."_verticapy_tmp_view_v_demo_b311bbb855a311ef880f0242ac120002_"', '"density", "sulphates"' USING PARAMETERS exclude_columns='', ntree=10, sampling_size=0.632, col_sample_by_tree=1, max_depth=3, nbins=6); ======= details ======= predictor| type ---------+---------------- density |float or numeric sulphates|float or numeric =============== Additional Info =============== Name |Value ------------------+----- tree_count | 10 rejected_row_count| 0 accepted_row_count|6497
To get the SQL query which uses Vertica functions use below:
model.decision_function(data) Out[4]: None fixed_acidity volatile_acidity citric_acid residual_sugar chlorides free_sulfur_dioxide total_sulfur_dioxide density pH sulphates \\ 1 3.8 0.31 0.02 11.1 0.036 20.0 114.0 0.99248 3.75 0.44 \\ 2 3.9 0.225 0.4 4.2 0.03 29.0 118.0 0.989 3.57 0.36 \\ 3 4.2 0.17 0.36 1.8 0.029 93.0 161.0 0.98999 3.65 0.89 \\ 4 4.2 0.215 0.23 5.1 0.041 64.0 157.0 0.99688 3.42 0.44 \\ 5 4.4 0.32 0.39 4.3 0.03 31.0 127.0 0.98904 3.46 0.36 \\ 6 4.4 0.46 0.1 2.8 0.024 31.0 111.0 0.98816 3.48 0.34 \\ 7 4.4 0.54 0.09 5.1 0.038 52.0 97.0 0.99022 3.41 0.4 \\ 8 4.5 0.19 0.21 0.95 0.033 89.0 159.0 0.99332 3.34 0.42 \\ 9 4.6 0.445 0.0 1.4 0.053 11.0 178.0 0.99426 3.79 0.55 \\ 10 4.6 0.52 0.15 2.1 0.054 8.0 65.0 0.9934 3.9 0.56 \\ 11 4.7 0.145 0.29 1.0 0.042 35.0 90.0 0.9908 3.76 0.49 \\ 12 4.7 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 \\ 13 4.7 0.455 0.18 1.9 0.036 33.0 106.0 0.98746 3.21 0.83 \\ 14 4.7 0.6 0.17 2.3 0.058 17.0 106.0 0.9932 3.85 0.6 \\ 15 4.7 0.67 0.09 1.0 0.02 5.0 9.0 0.98722 3.3 0.34 \\ 16 4.7 0.785 0.0 3.4 0.036 23.0 134.0 0.98981 3.53 0.92 \\ 17 4.8 0.13 0.32 1.2 0.042 40.0 98.0 0.9898 3.42 0.64 \\ 18 4.8 0.17 0.28 2.9 0.03 22.0 111.0 0.9902 3.38 0.34 \\ 19 4.8 0.21 0.21 10.2 0.037 17.0 112.0 0.99324 3.66 0.48 \\ 20 4.8 0.225 0.38 1.2 0.074 47.0 130.0 0.99132 3.31 0.4 \\ 21 4.8 0.26 0.23 10.6 0.034 23.0 111.0 0.99274 3.46 0.28 \\ 22 4.8 0.29 0.23 1.1 0.044 38.0 180.0 0.98924 3.28 0.34 \\ 23 4.8 0.33 0.0 6.5 0.028 34.0 163.0 0.9937 3.35 0.61 \\ 24 4.8 0.34 0.0 6.5 0.028 33.0 163.0 0.9939 3.36 0.61 \\ 25 4.8 0.65 0.12 1.1 0.013 4.0 10.0 0.99246 3.32 0.36 \\ 26 4.9 0.235 0.27 11.75 0.03 34.0 118.0 0.9954 3.07 0.5 \\ 27 4.9 0.33 0.31 1.2 0.016 39.0 150.0 0.98713 3.33 0.59 \\ 28 4.9 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 \\ 29 4.9 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 \\ 30 4.9 0.345 0.34 1.0 0.068 32.0 143.0 0.99138 3.24 0.4 \\ 31 4.9 0.345 0.34 1.0 0.068 32.0 143.0 0.99138 3.24 0.4 \\ 32 4.9 0.42 0.0 2.1 0.048 16.0 42.0 0.99154 3.71 0.74 \\ 33 4.9 0.47 0.17 1.9 0.035 60.0 148.0 0.98964 3.27 0.35 \\ 34 5.0 0.17 0.56 1.5 0.026 24.0 115.0 0.9906 3.48 0.39 \\ 35 5.0 0.2 0.4 1.9 0.015 20.0 98.0 0.9897 3.37 0.55 \\ 36 5.0 0.235 0.27 11.75 0.03 34.0 118.0 0.9954 3.07 0.5 \\ 37 5.0 0.24 0.19 5.0 0.043 17.0 101.0 0.99438 3.67 0.57 \\ 38 5.0 0.24 0.21 2.2 0.039 31.0 100.0 0.99098 3.69 0.62 \\ 39 5.0 0.24 0.34 1.1 0.034 49.0 158.0 0.98774 3.32 0.32 \\ 40 5.0 0.255 0.22 2.7 0.043 46.0 153.0 0.99238 3.75 0.76 \\ 41 5.0 0.27 0.32 4.5 0.032 58.0 178.0 0.98956 3.45 0.31 \\ 42 5.0 0.27 0.32 4.5 0.032 58.0 178.0 0.98956 3.45 0.31 \\ 43 5.0 0.27 0.4 1.2 0.076 42.0 124.0 0.99204 3.32 0.47 \\ 44 5.0 0.29 0.54 5.7 0.035 54.0 155.0 0.98976 3.27 0.34 \\ 45 5.0 0.3 0.33 3.7 0.03 54.0 173.0 0.9887 3.36 0.3 \\ 46 5.0 0.31 0.0 6.4 0.046 43.0 166.0 0.994 3.3 0.63 \\ 47 5.0 0.33 0.16 1.5 0.049 10.0 97.0 0.9917 3.48 0.44 \\ 48 5.0 0.33 0.16 1.5 0.049 10.0 97.0 0.9917 3.48 0.44 \\ 49 5.0 0.33 0.16 1.5 0.049 10.0 97.0 0.9917 3.48 0.44 \\ 50 5.0 0.33 0.18 4.6 0.032 40.0 124.0 0.99114 3.18 0.4 \\ 51 5.0 0.33 0.23 11.8 0.03 23.0 158.0 0.99322 3.41 0.64 \\ 52 5.0 0.35 0.25 7.8 0.031 24.0 116.0 0.99241 3.39 0.4 \\ 53 5.0 0.35 0.25 7.8 0.031 24.0 116.0 0.99241 3.39 0.4 \\ 54 5.0 0.38 0.01 1.6 0.048 26.0 60.0 0.99084 3.7 0.75 \\ 55 5.0 0.4 0.5 4.3 0.046 29.0 80.0 0.9902 3.49 0.66 \\ 56 5.0 0.42 0.24 2.0 0.06 19.0 50.0 0.9917 3.72 0.74 \\ 57 5.0 0.44 0.04 18.6 0.039 38.0 128.0 0.9985 3.37 0.57 \\ 58 5.0 0.455 0.18 1.9 0.036 33.0 106.0 0.98746 3.21 0.83 \\ 59 5.0 0.55 0.14 8.3 0.032 35.0 164.0 0.9918 3.53 0.51 \\ 60 5.0 0.61 0.12 1.3 0.009 65.0 100.0 0.9874 3.26 0.37 \\ 61 5.0 0.74 0.0 1.2 0.041 16.0 46.0 0.99258 4.01 0.59 \\ 62 5.0 1.02 0.04 1.4 0.045 41.0 85.0 0.9938 3.75 0.48 \\ 63 5.0 1.04 0.24 1.6 0.05 32.0 96.0 0.9934 3.74 0.62 \\ 64 5.1 0.11 0.32 1.6 0.028 12.0 90.0 0.99008 3.57 0.52 \\ 65 5.1 0.14 0.25 0.7 0.039 15.0 89.0 0.9919 3.22 0.43 \\ 66 5.1 0.165 0.22 5.7 0.047 42.0 146.0 0.9934 3.18 0.55 \\ 67 5.1 0.21 0.28 1.4 0.047 48.0 148.0 0.99168 3.5 0.49 \\ 68 5.1 0.23 0.18 1.0 0.053 13.0 99.0 0.98956 3.22 0.39 \\ 69 5.1 0.25 0.36 1.3 0.035 40.0 78.0 0.9891 3.23 0.64 \\ 70 5.1 0.26 0.33 1.1 0.027 46.0 113.0 0.98946 3.35 0.43 \\ 71 5.1 0.26 0.34 6.4 0.034 26.0 99.0 0.99449 3.23 0.41 \\ 72 5.1 0.29 0.28 8.3 0.026 27.0 107.0 0.99308 3.36 0.37 \\ 73 5.1 0.29 0.28 8.3 0.026 27.0 107.0 0.99308 3.36 0.37 \\ 74 5.1 0.3 0.3 2.3 0.048 40.0 150.0 0.98944 3.29 0.46 \\ 75 5.1 0.305 0.13 1.75 0.036 17.0 73.0 0.99 3.4 0.51 \\ 76 5.1 0.31 0.3 0.9 0.037 28.0 152.0 0.992 3.54 0.56 \\ 77 5.1 0.33 0.22 1.6 0.027 18.0 89.0 0.9893 3.51 0.38 \\ 78 5.1 0.33 0.22 1.6 0.027 18.0 89.0 0.9893 3.51 0.38 \\ 79 5.1 0.33 0.22 1.6 0.027 18.0 89.0 0.9893 3.51 0.38 \\ 80 5.1 0.33 0.27 6.7 0.022 44.0 129.0 0.99221 3.36 0.39 \\ 81 5.1 0.35 0.26 6.8 0.034 36.0 120.0 0.99188 3.38 0.4 \\ 82 5.1 0.35 0.26 6.8 0.034 36.0 120.0 0.99188 3.38 0.4 \\ 83 5.1 0.35 0.26 6.8 0.034 36.0 120.0 0.99188 3.38 0.4 \\ 84 5.1 0.39 0.21 1.7 0.027 15.0 72.0 0.9894 3.5 0.45 \\ 85 5.1 0.42 0.0 1.8 0.044 18.0 88.0 0.99157 3.68 0.73 \\ 86 5.1 0.42 0.01 1.5 0.017 25.0 102.0 0.9894 3.38 0.36 \\ 87 5.1 0.47 0.02 1.3 0.034 18.0 44.0 0.9921 3.9 0.62 \\ 88 5.1 0.51 0.18 2.1 0.042 16.0 101.0 0.9924 3.46 0.87 \\ 89 5.1 0.52 0.06 2.7 0.052 30.0 79.0 0.9932 3.32 0.43 \\ 90 5.1 0.585 0.0 1.7 0.044 14.0 86.0 0.99264 3.56 0.94 \\ 91 5.2 0.155 0.33 1.6 0.028 13.0 59.0 0.98975 3.3 0.84 \\ 92 5.2 0.155 0.33 1.6 0.028 13.0 59.0 0.98975 3.3 0.84 \\ 93 5.2 0.16 0.34 0.8 0.029 26.0 77.0 0.99155 3.25 0.51 \\ 94 5.2 0.17 0.27 0.7 0.03 11.0 68.0 0.99218 3.3 0.41 \\ 95 5.2 0.185 0.22 1.0 0.03 47.0 123.0 0.99218 3.55 0.44 \\ 96 5.2 0.2 0.27 3.2 0.047 16.0 93.0 0.99235 3.44 0.53 \\ 97 5.2 0.21 0.31 1.7 0.048 17.0 61.0 0.98953 3.24 0.37 \\ 98 5.2 0.22 0.46 6.2 0.066 41.0 187.0 0.99362 3.19 0.42 \\ 99 5.2 0.24 0.15 7.1 0.043 32.0 134.0 0.99378 3.24 0.48 \\ 100 5.2 0.24 0.45 3.8 0.027 21.0 128.0 0.992 3.55 0.49 \\ None alcohol quality good color isolationforest_public_verticapy_tmp_... 1 12.4 6 0 white 0.476461415282077 2 12.8 8 1 white 0.476461415282077 3 12.0 7 1 white 0.513058454070391 4 8.0 3 0 white 0.477622699054387 5 12.8 8 1 white 0.476461415282077 6 13.1 6 0 white 0.476461415282077 7 12.2 7 1 white 0.476461415282077 8 8.0 5 0 white 0.476461415282077 9 10.2 5 0 white 0.477536784232871 10 13.1 4 0 red 0.477536784232871 11 11.3 6 0 white 0.476461415282077 12 10.5 5 0 white 0.476461415282077 13 14.0 7 1 white 0.513058454070391 14 12.9 6 0 red 0.477536784232871 15 13.6 5 0 white 0.476461415282077 16 13.8 6 0 white 0.513058454070391 17 11.8 7 1 white 0.477536784232871 18 11.3 7 1 white 0.476461415282077 19 12.2 7 1 white 0.476461415282077 20 10.3 6 0 white 0.476461415282077 21 11.5 7 1 white 0.476461415282077 22 11.9 6 0 white 0.476461415282077 23 9.9 5 0 white 0.477536784232871 24 9.9 6 0 white 0.477536784232871 25 13.5 4 0 white 0.476461415282077 26 9.4 6 0 white 0.476461415282077 27 14.0 8 1 white 0.477536784232871 28 10.4666666666667 5 0 white 0.476461415282077 29 10.4666666666667 5 0 white 0.476461415282077 30 10.1 5 0 white 0.476461415282077 31 10.1 5 0 white 0.476461415282077 32 14.0 7 1 red 0.477536784232871 33 11.5 6 0 white 0.476461415282077 34 10.8 7 1 white 0.476461415282077 35 12.05 6 0 white 0.477536784232871 36 9.4 6 0 white 0.476461415282077 37 10.0 5 0 white 0.477536784232871 38 11.7 6 0 white 0.477536784232871 39 13.1 7 1 white 0.476461415282077 40 11.3 6 0 white 0.477536784232871 41 12.6 7 1 white 0.476461415282077 42 12.6 7 1 white 0.476461415282077 43 10.1 6 0 white 0.476461415282077 44 12.9 8 1 white 0.476461415282077 45 13.0 7 1 white 0.476461415282077 46 9.9 6 0 white 0.477536784232871 47 10.7 6 0 white 0.476461415282077 48 10.7 6 0 white 0.476461415282077 49 10.7 6 0 white 0.476461415282077 50 11.0 6 0 white 0.476461415282077 51 11.8 6 0 white 0.477536784232871 52 11.3 6 0 white 0.476461415282077 53 11.3 6 0 white 0.476461415282077 54 14.0 6 0 red 0.477536784232871 55 13.6 6 0 red 0.477536784232871 56 14.0 8 1 red 0.477536784232871 57 10.2 6 0 white 0.478936432142131 58 14.0 7 1 white 0.513058454070391 59 12.5 8 1 white 0.476461415282077 60 13.5 5 0 white 0.476461415282077 61 12.5 6 0 red 0.477536784232871 62 10.5 4 0 red 0.476461415282077 63 11.5 5 0 red 0.477536784232871 64 12.2 6 0 white 0.477536784232871 65 9.2 6 0 white 0.476461415282077 66 9.9 6 0 white 0.477536784232871 67 10.4 5 0 white 0.476461415282077 68 11.5 5 0 white 0.476461415282077 69 12.1 7 1 white 0.477536784232871 70 11.4 7 1 white 0.476461415282077 71 9.2 6 0 white 0.476461415282077 72 11.0 6 0 white 0.476461415282077 73 11.0 6 0 white 0.476461415282077 74 12.2 6 0 white 0.476461415282077 75 12.3333333333333 5 0 white 0.476461415282077 76 10.1 6 0 white 0.477536784232871 77 12.5 7 1 white 0.476461415282077 78 12.5 7 1 white 0.476461415282077 79 12.5 7 1 white 0.476461415282077 80 11.0 7 1 white 0.476461415282077 81 11.5 6 0 white 0.476461415282077 82 11.5 6 0 white 0.476461415282077 83 11.5 6 0 white 0.476461415282077 84 12.5 6 0 white 0.476461415282077 85 13.6 7 1 red 0.477536784232871 86 12.3 7 1 white 0.476461415282077 87 12.8 6 0 red 0.477536784232871 88 12.9 7 1 red 0.513058454070391 89 9.3 5 0 white 0.476461415282077 90 12.9 7 1 red 0.513058454070391 91 11.9 8 1 white 0.513058454070391 92 11.9 8 1 white 0.513058454070391 93 10.1 6 0 white 0.476461415282077 94 9.8 5 0 white 0.476461415282077 95 10.15 6 0 white 0.476461415282077 96 10.1 7 1 white 0.477536784232871 97 12.0 7 1 white 0.476461415282077 98 9.73333333333333 5 0 white 0.476461415282077 99 9.9 6 0 white 0.476461415282077 100 11.2 8 1 white 0.476461415282077 Rows: 1-100 | Columns: 15
Note
Refer to
IsolationForestfor more information about the different methods and usages.