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kfold cross validation linear regression

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Erol Masimov answered on March 20, 2022 Popularity 10/10 Helpfulness 3/10

answer kfold cross validation linear regression

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kfold cross validation linear regression

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#importing libraries
import pandas as pd
from sklearn.model_selection import KFold
from sklearn.linear_model import LinearRegression

#loading the dataset
df = pd.read_csv('data.csv')
X = df.iloc[:,:-1].values  #independent variables
y = df.iloc[:,-1].values   #dependent variable

#splitting the dataset into train and test set 
kf = KFold(n_splits=5, shuffle=True, random_state=0)  #5-fold cross validation 
for train_index, test_index in kf.split(X):

    X_train, X_test = X[train_index], X[test_index]  #training and testing data for independent variables 
    y_train, y_test = y[train_index], y[test_index]  #training and testing data for dependent variable

    #fitting linear regression to the training set    
    regressor = LinearRegression()   #creating an object of LinearRegression class 
    regressor.fit(X_train, y_train)  #fitting the linear regression model to our training set

    #predicting the test set results    
    y_pred = regressor.predict(X_test)   #predicted values of dependent variable for test set

Popularity 10/10 Helpfulness 3/10 Language python

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Tags: cross-validation k-fold linear-regression python

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Contributed on Dec 24 2022

Erol Masimov

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kfold cross validation sklearn

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import numpy as np
from sklearn.model_selection import KFold
X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
y = np.array([1, 2, 3, 4])
kf = KFold(n_splits=2)
kf.get_n_splits(X)

print(kf)

for train_index, test_index in kf.split(X):
    print("TRAIN:", train_index, "TEST:", test_index)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]

Popularity 10/10 Helpfulness 5/10 Language python

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Tags: cross-validation cros

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Contributed on Mar 20 2022

Said HR

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k fold cross validation in python

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from sklearn.model_selection import cross_val_score, cross_val_predict, KFold
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.metrics import mean_absolute_error, make_scorer

kf = KFold(n_splits=5, shuffle=True, random_state=42)
# splits = kf.split(X) # See how they are splitted, each split contains index of training and validation
# r-squared results for 5-fold cross validation score  
mae_scorer = make_scorer(mean_absolute_error)
scores = cross_val_score(your_model, X, y, cv=kf, scoring=mae_scorer)  # a list of error terms
avg_score = np.mean(scores)
# predicted_y results for 5-fold cross validation prediction
predicted_y = cross_val_predict(your_model, X, y, cv=5) # a list of predictions
avg_predicted_y = np.mean(predicted_y)

### example of ridge regression with grid search with k-fold cross validation
param_grid = {"alpha": np.arange(0.0001, 1, 10), "solver": ["sag", "lsqr"]}
ridge = Ridge()
ridge_cv = GridSearchCV(ridge, param_grid, cv=kf)
ridge_cv2 = RandomizedSearchCV(ridge, param_grid, cv=kf, n_iter=2)
ridge_cv.fit(X_train, y_train)

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Tags: cross-validation cros

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Contributed on Jan 17 2024

Innocent Iguana

0 Answers Avg Quality 2/10

kfold cross validation linear regression

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