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k fold cross validation in python

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Innocent Iguana answered on March 20, 2022 Popularity 9/10 Helpfulness 4/10

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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)

Popularity 9/10 Helpfulness 4/10 Language python

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Tags: cross-validation k-fold python

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Contributed on Jan 17 2024

Innocent Iguana

0 Answers Avg Quality 2/10

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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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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

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Contributed on Dec 24 2022

Erol Masimov

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k fold cross validation from scratch python

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from sklearn.model_selection import cross_val_score
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)
print("Accuracy: {:.2f} %".format(accuracies.mean()*100))
print("Standard Deviation: {:.2f} %".format(accuracies.std()*100))

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Contributed on May 05 2022

Trained Tuna

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k fold cross validation python from scratch

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#this function is used for KNN model. Where K represents the number of nearest neighbours.
def cross_validation(train_X, train_y, num_folds=4, k=1):
    dataset = list()
    dataset_split = list()
    val_acc = list()
    
    for i in range(len(train_X)):
        data = np.append(train_X[i],train_y[i])
        dataset.append(data)
    
    dataset_copy = list(dataset)
    fold_size = int(len(dataset) / num_folds)
    
    for i in range(num_folds):
        fold = list()
        while len(fold) < fold_size:
            index = randrange(len(dataset_copy))
            fold.append(dataset_copy.pop(index))
        dataset_split.append(fold)
        
    for folds in dataset_split:
        train_set= folds
        train_set = np.array(train_set)
        test_set = list()
        for row in folds:
            row_copy = list(row)
            test_set.append(row_copy)
            row_copy[-1] = None
        test_set = np.array(test_set)
        train_x = train_set[:, :-1]
        train_y = train_set[:,-1]
        test_x = test_set[:, :-1]
        predicted = predict(train_x,train_y, test_x, k)
        actual = [row[-1] for row in fold]
        accuracy = compute_accuracy(actual, predicted)
        val_acc.append(accuracy)
        
    val_acc_var = statistics.variance(val_acc)
    vall_acc = sum(val_acc)/len(val_acc)

    return vall_acc, val_acc_var
    
  #If this works, you can buy me a coffee.
# #https://www.buymeacoffee.com/eyolve

Popularity 9/10 Helpfulness 2/10 Language python

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Tags: cross-validation cros

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Contributed on Oct 16 2022

Muhammad Usman Shakeel

0 Answers Avg Quality 2/10

K fold Cross Validation

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# K-Fold Cross-Validation
from sklearn.model_selection import cross_validate
def cross_validation(model, _X, _y, _cv=5):
      '''Function to perform 5 Folds Cross-Validation
       Parameters
       ----------
      model: Python Class, default=None
              This is the machine learning algorithm to be used for training.
      _X: array
           This is the matrix of features.
      _y: array
           This is the target variable.
      _cv: int, default=5
          Determines the number of folds for cross-validation.
       Returns
       -------
       The function returns a dictionary containing the metrics 'accuracy', 'precision',
       'recall', 'f1' for both training set and validation set.
      '''
      _scoring = ['accuracy', 'precision', 'recall', 'f1']
      results = cross_validate(estimator=model,
                               X=_X,
                               y=_y,
                               cv=_cv,
                               scoring=_scoring,
                               return_train_score=True)
      
      return {"Training Accuracy scores": results['train_accuracy'],
              "Mean Training Accuracy": results['train_accuracy'].mean()*100,
              "Training Precision scores": results['train_precision'],
              "Mean Training Precision": results['train_precision'].mean(),
              "Training Recall scores": results['train_recall'],
              "Mean Training Recall": results['train_recall'].mean(),
              "Training F1 scores": results['train_f1'],
              "Mean Training F1 Score": results['train_f1'].mean(),
              "Validation Accuracy scores": results['test_accuracy'],
              "Mean Validation Accuracy": results['test_accuracy'].mean()*100,
              "Validation Precision scores": results['test_precision'],
              "Mean Validation Precision": results['test_precision'].mean(),
              "Validation Recall scores": results['test_recall'],
              "Mean Validation Recall": results['test_recall'].mean(),
              "Validation F1 scores": results['test_f1'],
              "Mean Validation F1 Score": results['test_f1'].mean()
              }

Popularity 10/10 Helpfulness 1/10 Language python

Source: www.section.io

Tags: cross-validation cros

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Contributed on Jun 28 2022

Mert Bozkir

0 Answers Avg Quality 2/10

k fold cross validation in python

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