df = data.frame

Now, we are using the following Python statements to create two DataFrames, one for features and one for the output labels.

df_labels = df[["MedHouseVal"]]
df_features = df.drop(["MedHouseVal"], axis=1)
print(df_features.head())
print(df_labels.head())

Now, we will use the RandomForestRegressor and ModelSelection class from the sklearn library to perform feature selection based on model performance.

from sklearn.feature_selection import SelectFromModel
from sklearn.datasets import fetch_california_housing
from sklearn.ensemble import RandomForestRegressor

data = fetch_california_housing(as_frame=True)
df = data.frame
print(df.info())
print(df.head())

df_labels = df[["MedHouseVal"]]
df_features = df.drop(["MedHouseVal"], axis=1)
print(df_features.head())
print(df_labels.head())

random_forest_regressor = RandomForestRegressor()
random_forest_regressor.fit(df_features.values, df_labels["MedHouseVal"])

model = SelectFromModel(random_forest_regressor, prefit=True, threshold="mean")
X_transformed = model.transform(df_features.values)

selected_features = model.get_support(indices=True)
print(“Selected Features: “, selected_features)

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

Please note that prefit=True parameter indicates that a prefit model is directly passed into the constructor. And the threshold=”mean” parameter indicates that the threshold used to select features is the mean value. So, features whose absolute importance value is greater or equal are kept and the rest are discarded…