Feature selection based on model performance using sklearn

by Amrita Mitra | Jan 13, 2023 | AI, Machine Learning and Deep Learning, Featured, Machine Learning Using Python, Python Scikit-learn

The get_support() function here returns the indices of the selected features. We are using the following Python statements to create another DataFrame that contains only the selected features and the output label.

df2 = df[df.columns[selected_features]]
df2["MedHouseVal"] = df["MedHouseVal"]
print(df2.head())

The output of the above program will be:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20640 entries, 0 to 20639
Data columns (total 9 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   MedInc       20640 non-null  float64
 1   HouseAge     20640 non-null  float64
 2   AveRooms     20640 non-null  float64
 3   AveBedrms    20640 non-null  float64
 4   Population   20640 non-null  float64
 5   AveOccup     20640 non-null  float64
 6   Latitude     20640 non-null  float64
 7   Longitude    20640 non-null  float64
 8   MedHouseVal  20640 non-null  float64
dtypes: float64(9)
memory usage: 1.4 MB
None
   MedInc  HouseAge  AveRooms  ...  Latitude  Longitude  MedHouseVal
0  8.3252      41.0  6.984127  ...     37.88    -122.23        4.526
1  8.3014      21.0  6.238137  ...     37.86    -122.22        3.585
2  7.2574      52.0  8.288136  ...     37.85    -122.24        3.521
3  5.6431      52.0  5.817352  ...     37.85    -122.25        3.413
4  3.8462      52.0  6.281853  ...     37.85    -122.25        3.422

[5 rows x 9 columns]
   MedInc  HouseAge  AveRooms  ...  AveOccup  Latitude  Longitude
0  8.3252      41.0  6.984127  ...  2.555556     37.88    -122.23
1  8.3014      21.0  6.238137  ...  2.109842     37.86    -122.22
2  7.2574      52.0  8.288136  ...  2.802260     37.85    -122.24
3  5.6431      52.0  5.817352  ...  2.547945     37.85    -122.25
4  3.8462      52.0  6.281853  ...  2.181467     37.85    -122.25

[5 rows x 8 columns]
   MedHouseVal
0        4.526
1        3.585
2        3.521
3        3.413
4        3.422
Selected Features: [0 5]
   MedInc  AveOccup  MedHouseVal
0  8.3252  2.555556        4.526
1  8.3014  2.109842        3.585
2  7.2574  2.802260        3.521
3  5.6431  2.547945        3.413
4  3.8462  2.181467        3.422

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