model_validation.py

import pandas as pd

from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score, KFold, StratifiedKFold

my_df = pd.read_csv("feature_selection_sample_data.csv")


# Test/Trial Split

X = my_df.drop(["output"], axis = 1)
y = my_df["output"]

# Regression Model

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)

regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
r2_score(y_test, y_pred)


# Cross Validation

cv_scores = cross_val_score(regressor, X, y, cv = 4, scoring = "r2")
cv_scores.mean()


# Regression Problem

cv = KFold(n_splits = 4, shuffle = True, random_state = 42)
cv_scores = cross_val_score(regressor, X, y, cv = cv, scoring = "r2")
cv_scores.mean()





# Classification Model (just add stratify = y)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42, stratify = y)


# Classification Problem

cv = StratifiedKFold(n_splits = 4, shuffle = True, random_state = 42)
cv_scores = cross_val_score(clf, X, y, cv = cv, scoring = "accuracy")
cv_scores.mean()