verticapy.machine_learning.vertica.ensemble.IsolationForest.decision_function#
- IsolationForest.decision_function(vdf: str | vDataFrame, X: str | list[str] | 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_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: 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"])
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 \\ 1 3.8 0.31 0.02 11.1 \\ 2 3.9 0.225 0.4 4.2 \\ 3 4.2 0.17 0.36 1.8 \\ 4 4.2 0.215 0.23 5.1 \\ 5 4.4 0.32 0.39 4.3 \\ 6 4.4 0.46 0.1 2.8 \\ 7 4.4 0.54 0.09 5.1 \\ 8 4.5 0.19 0.21 0.95 \\ 9 4.6 0.445 0.0 1.4 \\ 10 4.6 0.52 0.15 2.1 \\ 11 4.7 0.145 0.29 1.0 \\ 12 4.7 0.335 0.14 1.3 \\ 13 4.7 0.455 0.18 1.9 \\ 14 4.7 0.6 0.17 2.3 \\ 15 4.7 0.67 0.09 1.0 \\ 16 4.7 0.785 0.0 3.4 \\ 17 4.8 0.13 0.32 1.2 \\ 18 4.8 0.17 0.28 2.9 \\ 19 4.8 0.21 0.21 10.2 \\ 20 4.8 0.225 0.38 1.2 \\ 21 4.8 0.26 0.23 10.6 \\ 22 4.8 0.29 0.23 1.1 \\ 23 4.8 0.33 0.0 6.5 \\ 24 4.8 0.34 0.0 6.5 \\ 25 4.8 0.65 0.12 1.1 \\ 26 4.9 0.235 0.27 11.75 \\ 27 4.9 0.33 0.31 1.2 \\ 28 4.9 0.335 0.14 1.3 \\ 29 4.9 0.335 0.14 1.3 \\ 30 4.9 0.345 0.34 1.0 \\ 31 4.9 0.345 0.34 1.0 \\ 32 4.9 0.42 0.0 2.1 \\ 33 4.9 0.47 0.17 1.9 \\ 34 5.0 0.17 0.56 1.5 \\ 35 5.0 0.2 0.4 1.9 \\ 36 5.0 0.235 0.27 11.75 \\ 37 5.0 0.24 0.19 5.0 \\ 38 5.0 0.24 0.21 2.2 \\ 39 5.0 0.24 0.34 1.1 \\ 40 5.0 0.255 0.22 2.7 \\ 41 5.0 0.27 0.32 4.5 \\ 42 5.0 0.27 0.32 4.5 \\ 43 5.0 0.27 0.4 1.2 \\ 44 5.0 0.29 0.54 5.7 \\ 45 5.0 0.3 0.33 3.7 \\ 46 5.0 0.31 0.0 6.4 \\ 47 5.0 0.33 0.16 1.5 \\ 48 5.0 0.33 0.16 1.5 \\ 49 5.0 0.33 0.16 1.5 \\ 50 5.0 0.33 0.18 4.6 \\ 51 5.0 0.33 0.23 11.8 \\ 52 5.0 0.35 0.25 7.8 \\ 53 5.0 0.35 0.25 7.8 \\ 54 5.0 0.38 0.01 1.6 \\ 55 5.0 0.4 0.5 4.3 \\ 56 5.0 0.42 0.24 2.0 \\ 57 5.0 0.44 0.04 18.6 \\ 58 5.0 0.455 0.18 1.9 \\ 59 5.0 0.55 0.14 8.3 \\ 60 5.0 0.61 0.12 1.3 \\ 61 5.0 0.74 0.0 1.2 \\ 62 5.0 1.02 0.04 1.4 \\ 63 5.0 1.04 0.24 1.6 \\ 64 5.1 0.11 0.32 1.6 \\ 65 5.1 0.14 0.25 0.7 \\ 66 5.1 0.165 0.22 5.7 \\ 67 5.1 0.21 0.28 1.4 \\ 68 5.1 0.23 0.18 1.0 \\ 69 5.1 0.25 0.36 1.3 \\ 70 5.1 0.26 0.33 1.1 \\ 71 5.1 0.26 0.34 6.4 \\ 72 5.1 0.29 0.28 8.3 \\ 73 5.1 0.29 0.28 8.3 \\ 74 5.1 0.3 0.3 2.3 \\ 75 5.1 0.305 0.13 1.75 \\ 76 5.1 0.31 0.3 0.9 \\ 77 5.1 0.33 0.22 1.6 \\ 78 5.1 0.33 0.22 1.6 \\ 79 5.1 0.33 0.22 1.6 \\ 80 5.1 0.33 0.27 6.7 \\ 81 5.1 0.35 0.26 6.8 \\ 82 5.1 0.35 0.26 6.8 \\ 83 5.1 0.35 0.26 6.8 \\ 84 5.1 0.39 0.21 1.7 \\ 85 5.1 0.42 0.0 1.8 \\ 86 5.1 0.42 0.01 1.5 \\ 87 5.1 0.47 0.02 1.3 \\ 88 5.1 0.51 0.18 2.1 \\ 89 5.1 0.52 0.06 2.7 \\ 90 5.1 0.585 0.0 1.7 \\ 91 5.2 0.155 0.33 1.6 \\ 92 5.2 0.155 0.33 1.6 \\ 93 5.2 0.16 0.34 0.8 \\ 94 5.2 0.17 0.27 0.7 \\ 95 5.2 0.185 0.22 1.0 \\ 96 5.2 0.2 0.27 3.2 \\ 97 5.2 0.21 0.31 1.7 \\ 98 5.2 0.22 0.46 6.2 \\ 99 5.2 0.24 0.15 7.1 \\ 100 5.2 0.24 0.45 3.8 \\ None chlorides free_sulfur_dioxide total_sulfur_dioxide \\ 1 0.036 20.0 114.0 \\ 2 0.03 29.0 118.0 \\ 3 0.029 93.0 161.0 \\ 4 0.041 64.0 157.0 \\ 5 0.03 31.0 127.0 \\ 6 0.024 31.0 111.0 \\ 7 0.038 52.0 97.0 \\ 8 0.033 89.0 159.0 \\ 9 0.053 11.0 178.0 \\ 10 0.054 8.0 65.0 \\ 11 0.042 35.0 90.0 \\ 12 0.036 69.0 168.0 \\ 13 0.036 33.0 106.0 \\ 14 0.058 17.0 106.0 \\ 15 0.02 5.0 9.0 \\ 16 0.036 23.0 134.0 \\ 17 0.042 40.0 98.0 \\ 18 0.03 22.0 111.0 \\ 19 0.037 17.0 112.0 \\ 20 0.074 47.0 130.0 \\ 21 0.034 23.0 111.0 \\ 22 0.044 38.0 180.0 \\ 23 0.028 34.0 163.0 \\ 24 0.028 33.0 163.0 \\ 25 0.013 4.0 10.0 \\ 26 0.03 34.0 118.0 \\ 27 0.016 39.0 150.0 \\ 28 0.036 69.0 168.0 \\ 29 0.036 69.0 168.0 \\ 30 0.068 32.0 143.0 \\ 31 0.068 32.0 143.0 \\ 32 0.048 16.0 42.0 \\ 33 0.035 60.0 148.0 \\ 34 0.026 24.0 115.0 \\ 35 0.015 20.0 98.0 \\ 36 0.03 34.0 118.0 \\ 37 0.043 17.0 101.0 \\ 38 0.039 31.0 100.0 \\ 39 0.034 49.0 158.0 \\ 40 0.043 46.0 153.0 \\ 41 0.032 58.0 178.0 \\ 42 0.032 58.0 178.0 \\ 43 0.076 42.0 124.0 \\ 44 0.035 54.0 155.0 \\ 45 0.03 54.0 173.0 \\ 46 0.046 43.0 166.0 \\ 47 0.049 10.0 97.0 \\ 48 0.049 10.0 97.0 \\ 49 0.049 10.0 97.0 \\ 50 0.032 40.0 124.0 \\ 51 0.03 23.0 158.0 \\ 52 0.031 24.0 116.0 \\ 53 0.031 24.0 116.0 \\ 54 0.048 26.0 60.0 \\ 55 0.046 29.0 80.0 \\ 56 0.06 19.0 50.0 \\ 57 0.039 38.0 128.0 \\ 58 0.036 33.0 106.0 \\ 59 0.032 35.0 164.0 \\ 60 0.009 65.0 100.0 \\ 61 0.041 16.0 46.0 \\ 62 0.045 41.0 85.0 \\ 63 0.05 32.0 96.0 \\ 64 0.028 12.0 90.0 \\ 65 0.039 15.0 89.0 \\ 66 0.047 42.0 146.0 \\ 67 0.047 48.0 148.0 \\ 68 0.053 13.0 99.0 \\ 69 0.035 40.0 78.0 \\ 70 0.027 46.0 113.0 \\ 71 0.034 26.0 99.0 \\ 72 0.026 27.0 107.0 \\ 73 0.026 27.0 107.0 \\ 74 0.048 40.0 150.0 \\ 75 0.036 17.0 73.0 \\ 76 0.037 28.0 152.0 \\ 77 0.027 18.0 89.0 \\ 78 0.027 18.0 89.0 \\ 79 0.027 18.0 89.0 \\ 80 0.022 44.0 129.0 \\ 81 0.034 36.0 120.0 \\ 82 0.034 36.0 120.0 \\ 83 0.034 36.0 120.0 \\ 84 0.027 15.0 72.0 \\ 85 0.044 18.0 88.0 \\ 86 0.017 25.0 102.0 \\ 87 0.034 18.0 44.0 \\ 88 0.042 16.0 101.0 \\ 89 0.052 30.0 79.0 \\ 90 0.044 14.0 86.0 \\ 91 0.028 13.0 59.0 \\ 92 0.028 13.0 59.0 \\ 93 0.029 26.0 77.0 \\ 94 0.03 11.0 68.0 \\ 95 0.03 47.0 123.0 \\ 96 0.047 16.0 93.0 \\ 97 0.048 17.0 61.0 \\ 98 0.066 41.0 187.0 \\ 99 0.043 32.0 134.0 \\ 100 0.027 21.0 128.0 \\ None density pH sulphates alcohol quality \\ 1 0.99248 3.75 0.44 12.4 6 \\ 2 0.989 3.57 0.36 12.8 8 \\ 3 0.98999 3.65 0.89 12.0 7 \\ 4 0.99688 3.42 0.44 8.0 3 \\ 5 0.98904 3.46 0.36 12.8 8 \\ 6 0.98816 3.48 0.34 13.1 6 \\ 7 0.99022 3.41 0.4 12.2 7 \\ 8 0.99332 3.34 0.42 8.0 5 \\ 9 0.99426 3.79 0.55 10.2 5 \\ 10 0.9934 3.9 0.56 13.1 4 \\ 11 0.9908 3.76 0.49 11.3 6 \\ 12 0.99212 3.47 0.46 10.5 5 \\ 13 0.98746 3.21 0.83 14.0 7 \\ 14 0.9932 3.85 0.6 12.9 6 \\ 15 0.98722 3.3 0.34 13.6 5 \\ 16 0.98981 3.53 0.92 13.8 6 \\ 17 0.9898 3.42 0.64 11.8 7 \\ 18 0.9902 3.38 0.34 11.3 7 \\ 19 0.99324 3.66 0.48 12.2 7 \\ 20 0.99132 3.31 0.4 10.3 6 \\ 21 0.99274 3.46 0.28 11.5 7 \\ 22 0.98924 3.28 0.34 11.9 6 \\ 23 0.9937 3.35 0.61 9.9 5 \\ 24 0.9939 3.36 0.61 9.9 6 \\ 25 0.99246 3.32 0.36 13.5 4 \\ 26 0.9954 3.07 0.5 9.4 6 \\ 27 0.98713 3.33 0.59 14.0 8 \\ 28 0.99212 3.47 0.46 10.4666666666667 5 \\ 29 0.99212 3.47 0.46 10.4666666666667 5 \\ 30 0.99138 3.24 0.4 10.1 5 \\ 31 0.99138 3.24 0.4 10.1 5 \\ 32 0.99154 3.71 0.74 14.0 7 \\ 33 0.98964 3.27 0.35 11.5 6 \\ 34 0.9906 3.48 0.39 10.8 7 \\ 35 0.9897 3.37 0.55 12.05 6 \\ 36 0.9954 3.07 0.5 9.4 6 \\ 37 0.99438 3.67 0.57 10.0 5 \\ 38 0.99098 3.69 0.62 11.7 6 \\ 39 0.98774 3.32 0.32 13.1 7 \\ 40 0.99238 3.75 0.76 11.3 6 \\ 41 0.98956 3.45 0.31 12.6 7 \\ 42 0.98956 3.45 0.31 12.6 7 \\ 43 0.99204 3.32 0.47 10.1 6 \\ 44 0.98976 3.27 0.34 12.9 8 \\ 45 0.9887 3.36 0.3 13.0 7 \\ 46 0.994 3.3 0.63 9.9 6 \\ 47 0.9917 3.48 0.44 10.7 6 \\ 48 0.9917 3.48 0.44 10.7 6 \\ 49 0.9917 3.48 0.44 10.7 6 \\ 50 0.99114 3.18 0.4 11.0 6 \\ 51 0.99322 3.41 0.64 11.8 6 \\ 52 0.99241 3.39 0.4 11.3 6 \\ 53 0.99241 3.39 0.4 11.3 6 \\ 54 0.99084 3.7 0.75 14.0 6 \\ 55 0.9902 3.49 0.66 13.6 6 \\ 56 0.9917 3.72 0.74 14.0 8 \\ 57 0.9985 3.37 0.57 10.2 6 \\ 58 0.98746 3.21 0.83 14.0 7 \\ 59 0.9918 3.53 0.51 12.5 8 \\ 60 0.9874 3.26 0.37 13.5 5 \\ 61 0.99258 4.01 0.59 12.5 6 \\ 62 0.9938 3.75 0.48 10.5 4 \\ 63 0.9934 3.74 0.62 11.5 5 \\ 64 0.99008 3.57 0.52 12.2 6 \\ 65 0.9919 3.22 0.43 9.2 6 \\ 66 0.9934 3.18 0.55 9.9 6 \\ 67 0.99168 3.5 0.49 10.4 5 \\ 68 0.98956 3.22 0.39 11.5 5 \\ 69 0.9891 3.23 0.64 12.1 7 \\ 70 0.98946 3.35 0.43 11.4 7 \\ 71 0.99449 3.23 0.41 9.2 6 \\ 72 0.99308 3.36 0.37 11.0 6 \\ 73 0.99308 3.36 0.37 11.0 6 \\ 74 0.98944 3.29 0.46 12.2 6 \\ 75 0.99 3.4 0.51 12.3333333333333 5 \\ 76 0.992 3.54 0.56 10.1 6 \\ 77 0.9893 3.51 0.38 12.5 7 \\ 78 0.9893 3.51 0.38 12.5 7 \\ 79 0.9893 3.51 0.38 12.5 7 \\ 80 0.99221 3.36 0.39 11.0 7 \\ 81 0.99188 3.38 0.4 11.5 6 \\ 82 0.99188 3.38 0.4 11.5 6 \\ 83 0.99188 3.38 0.4 11.5 6 \\ 84 0.9894 3.5 0.45 12.5 6 \\ 85 0.99157 3.68 0.73 13.6 7 \\ 86 0.9894 3.38 0.36 12.3 7 \\ 87 0.9921 3.9 0.62 12.8 6 \\ 88 0.9924 3.46 0.87 12.9 7 \\ 89 0.9932 3.32 0.43 9.3 5 \\ 90 0.99264 3.56 0.94 12.9 7 \\ 91 0.98975 3.3 0.84 11.9 8 \\ 92 0.98975 3.3 0.84 11.9 8 \\ 93 0.99155 3.25 0.51 10.1 6 \\ 94 0.99218 3.3 0.41 9.8 5 \\ 95 0.99218 3.55 0.44 10.15 6 \\ 96 0.99235 3.44 0.53 10.1 7 \\ 97 0.98953 3.24 0.37 12.0 7 \\ 98 0.99362 3.19 0.42 9.73333333333333 5 \\ 99 0.99378 3.24 0.48 9.9 6 \\ 100 0.992 3.55 0.49 11.2 8 \\ None good color isolationforest_public_verticapy_tmp_... 1 0 white 0.479932722103834 2 1 white 0.479932722103834 3 1 white 0.540564858548604 4 0 white 0.484554883412372 5 1 white 0.479932722103834 6 0 white 0.479932722103834 7 1 white 0.479932722103834 8 0 white 0.479932722103834 9 0 white 0.477862056499846 10 0 red 0.477862056499846 11 0 white 0.479932722103834 12 0 white 0.479932722103834 13 1 white 0.540564858548604 14 0 red 0.477862056499846 15 0 white 0.479932722103834 16 0 white 0.540564858548604 17 1 white 0.477862056499846 18 1 white 0.479932722103834 19 1 white 0.479932722103834 20 0 white 0.479932722103834 21 1 white 0.479932722103834 22 0 white 0.479932722103834 23 0 white 0.477862056499846 24 0 white 0.477862056499846 25 0 white 0.479932722103834 26 0 white 0.479932722103834 27 1 white 0.477862056499846 28 0 white 0.479932722103834 29 0 white 0.479932722103834 30 0 white 0.479932722103834 31 0 white 0.479932722103834 32 1 red 0.477862056499846 33 0 white 0.479932722103834 34 1 white 0.479932722103834 35 0 white 0.477862056499846 36 0 white 0.479932722103834 37 0 white 0.477862056499846 38 0 white 0.477862056499846 39 1 white 0.479932722103834 40 0 white 0.477862056499846 41 1 white 0.479932722103834 42 1 white 0.479932722103834 43 0 white 0.479932722103834 44 1 white 0.479932722103834 45 1 white 0.479932722103834 46 0 white 0.477862056499846 47 0 white 0.479932722103834 48 0 white 0.479932722103834 49 0 white 0.479932722103834 50 0 white 0.479932722103834 51 0 white 0.477862056499846 52 0 white 0.479932722103834 53 0 white 0.479932722103834 54 0 red 0.477862056499846 55 0 red 0.477862056499846 56 1 red 0.477862056499846 57 0 white 0.477859854068755 58 1 white 0.540564858548604 59 1 white 0.479932722103834 60 0 white 0.479932722103834 61 0 red 0.477862056499846 62 0 red 0.479932722103834 63 0 red 0.477862056499846 64 0 white 0.477862056499846 65 0 white 0.479932722103834 66 0 white 0.477862056499846 67 0 white 0.479932722103834 68 0 white 0.479932722103834 69 1 white 0.477862056499846 70 1 white 0.479932722103834 71 0 white 0.479932722103834 72 0 white 0.479932722103834 73 0 white 0.479932722103834 74 0 white 0.479932722103834 75 0 white 0.479932722103834 76 0 white 0.477862056499846 77 1 white 0.479932722103834 78 1 white 0.479932722103834 79 1 white 0.479932722103834 80 1 white 0.479932722103834 81 0 white 0.479932722103834 82 0 white 0.479932722103834 83 0 white 0.479932722103834 84 0 white 0.479932722103834 85 1 red 0.477862056499846 86 1 white 0.479932722103834 87 0 red 0.477862056499846 88 1 red 0.540564858548604 89 0 white 0.479932722103834 90 1 red 0.540564858548604 91 1 white 0.540564858548604 92 1 white 0.540564858548604 93 0 white 0.479932722103834 94 0 white 0.479932722103834 95 0 white 0.479932722103834 96 1 white 0.477862056499846 97 1 white 0.479932722103834 98 0 white 0.479932722103834 99 0 white 0.479932722103834 100 1 white 0.479932722103834 Rows: 1-100 | Columns: 15
Note
Refer to
IsolationForestfor more information about the different methods and usages.