The Code for k-Fold Cross-Validation is : #Import models from scikit learn module: from sklearn import metrics from sklearn.model_selection import KFold #Generic function for making a classification model and accessing performance: def classification_model(model, data, predictors, outcome): #Fit the model: model.fit(data[predictors],data[outcome]) #Make predictions on training set: predictions = model.predict(data[predictors]) #Print accuracy accuracy = metrics.accuracy_score(predictions,data[outcome]) print ("Accuracy ..

#### Category : k-fold

How can I make "Repeated" holdout method, I made holdout method and get accuracy but need to repeat holdout method for 30 times There is my code for holdout method [IN] X_train, X_test, Y_train, Y_test = train_test_split(X, Y.values.ravel(), random_state=100) model = LogisticRegression() model.fit(X_train, Y_train) result = model.score(X_test, Y_test) print("Accuracy: %.2f%%" % (result*100.0)) [OUT] Accuracy: 49.62% ..

I am trying to do nested kfold validation from scratch. I have a myCrossVal function that does the cross validation, but in order to do a nested one I need to pass the knn object to it. However, myCrossVal function has parameters of X,y,k where X is a data matrix of size (samples, features) and ..

I have an image dataset with two classes. I want to implement k-fold cross-validation. but don’t know how to. Can anyone please help me.? The dataset is spliced into a train and a test set. and each set has two classes. Source: Python..

I’m trying to split the data set using the stratified k-fold and multinomial logistic regression but I got the mentioned error when I use this code: i=1 for train_set,test_set in skf.split(x,y): print(‘{} of SKFold {}’.format(i,skf.n_splits)) xtr,xvl = x.iloc[train_set],x.iloc[test_set] ytr,yvl = y.iloc[train_set],y.iloc[test_set] #model model.fit(xtr,ytr) score = roc_auc_score(yvl, model.predict(xvl), multi_class=’ovr’) print(‘ROC AUC score:’,score) cv_score.append(score) pred_test = model.predict_proba(x_test)[:,1] ..

Each tuple in this list should consist of a train_indices list and a test_indices list containing the training/testing data point indices for that particular K th split. Below is what we want to achieve with the dataset: data_indices = [(list_of_train_indices_for_split_1, list_of_test_indices_for_split_1) (list_of_train_indices_for_split_2, list_of_test_indices_for_split_2) (list_of_train_indices_for_split_3, list_of_test_indices_for_split_3) … … (list_of_train_indices_for_split_K, list_of_test_indices_for_split_K)] Here is my current function: def ..

I am experiecint troubles with using the Train/Test split of SKLEARN KFold. ValueError: Found input variables with inconsistent numbers of samples: [27328, 1301] My Code is the following: X = np.array(train_vecs) y = np.array(jsondata) kf = KFold(5, shuffle=True, random_state=42) cv_lr_f1 = [] for train_ind, val_ind in kf.split(X, y): X_train, y_train = X[train_ind], y[train_ind] X_val, y_val ..

This is my first time using KFold with a Neural Network, and I am struggling to understand why the performance changes pretty vastly dependent upon how many folds I am using. I am worried that there is an error in my code and maybe I am missing something. The code is as follows: import pandas ..

I am running a decision tree model, I used K-fold cross validation to randomly splits the training set into K distinct subsets (K = 10) How do I calculate average training error and average cross validation error? Here is my code so far, I am new to machine learning. data_tree_copy <- data_tree[sample(nrow(data_tree)),] ## Create 10 ..

I want to do a k-fold cross validation on a dataset of 20 points and i was wondering, what would be a good value for k? I read a little bit about it and it seems like 5 could be a good value, but i was worried that 4 points for each fold would be ..

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