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November 2, 2018 09:56
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import pandas as pd | |
import matplotlib.pyplot as plt | |
import numpy | |
from sklearn.linear_model import LinearRegression | |
from sklearn.tree import DecisionTreeRegressor | |
from sklearn.model_selection import train_test_split,cross_val_score | |
from sklearn.metrics import mean_squared_error | |
from math import sqrt | |
from sklearn.feature_selection import RFE | |
from sklearn.datasets import make_friedman1 | |
df = pd.read_csv('./training.csv') | |
X = df[list(df.columns)[:-1]] | |
y = df['A'] | |
X_train, X_test, y_train, y_test = train_test_split(X, y) | |
regressor = DecisionTreeRegressor(min_samples_split=3,max_depth=None) | |
regressor.fit(X_train, y_train) | |
y_predictions = regressor.predict(X_test) | |
print ("Selected Features for DecisionTree",regressor.feature_importances_) | |
# RFE Technique - Recursive Feature Elimination | |
X, y = make_friedman1(n_samples=20, n_features=17, random_state=0) | |
selector = RFE(LinearRegression()) | |
selector = selector.fit(X, y) | |
print ("Selected Features for LinearRegression",selector.ranking_) |
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