cross-validation

问题 When I want to evaluate my model with cross validation, should I perform cross validation on original (data thats not split on train and test) or on train / test data? I know that training data is used for fitting the model, and testing for evaluating. If I use cross validation, should I still split the data into train and test, or not? features = df.iloc[:,4:-1] results = df.iloc[:,-1] x_train, x_test, y_train, y_test = train_test_split(features, results, test_size=0.3, random_state=0) clf =

问题 I am trying to use cross_val_score with a customized estimator. It is important that this estimator receives a member variable which can be used later inside the fit function. But it seems inside cross_val_score the member variables are destroyed (or a new instance of the estimator is being created). Here is the minimal code which can reproduce the error: from sklearn.model_selection import cross_val_score from sklearn.base import BaseEstimator class MyEstimator(BaseEstimator): def __init__

问题 I want to apply k-fold cross validation on the following regression models: Linear Regression Polynomial Regression Support Vector Regression Decision Tree Regression Random Forest Regression I am able to apply k-fold cross validation on all except polynomial regression which gives me this error PolynomialFeatures' object has no attribute 'predict . How to work around this issue. Also am I doing the job correctly, actually my main motive is to see which model is performing better, so is there

问题 I want to apply k-fold cross validation on the following regression models: Linear Regression Polynomial Regression Support Vector Regression Decision Tree Regression Random Forest Regression I am able to apply k-fold cross validation on all except polynomial regression which gives me this error PolynomialFeatures' object has no attribute 'predict . How to work around this issue. Also am I doing the job correctly, actually my main motive is to see which model is performing better, so is there

问题 It seems that k-fold cross validation in convn net is not taken seriously due to huge running time of the neural network. I have a small data-set and I am interested in doing k-fold cross validation using the example given here. Is it possible? Thanks. 回答1: If you are using images with data generators, here's one way to do 10-fold cross-validation with Keras and scikit-learn. The strategy is to copy the files to training , validation , and test subfolders according to each fold. import numpy

问题 It seems that k-fold cross validation in convn net is not taken seriously due to huge running time of the neural network. I have a small data-set and I am interested in doing k-fold cross validation using the example given here. Is it possible? Thanks. 回答1: If you are using images with data generators, here's one way to do 10-fold cross-validation with Keras and scikit-learn. The strategy is to copy the files to training , validation , and test subfolders according to each fold. import numpy

问题 It seems that k-fold cross validation in convn net is not taken seriously due to huge running time of the neural network. I have a small data-set and I am interested in doing k-fold cross validation using the example given here. Is it possible? Thanks. 回答1: If you are using images with data generators, here's one way to do 10-fold cross-validation with Keras and scikit-learn. The strategy is to copy the files to training , validation , and test subfolders according to each fold. import numpy

问题 It seems that k-fold cross validation in convn net is not taken seriously due to huge running time of the neural network. I have a small data-set and I am interested in doing k-fold cross validation using the example given here. Is it possible? Thanks. 回答1: If you are using images with data generators, here's one way to do 10-fold cross-validation with Keras and scikit-learn. The strategy is to copy the files to training , validation , and test subfolders according to each fold. import numpy

问题 It seems that k-fold cross validation in convn net is not taken seriously due to huge running time of the neural network. I have a small data-set and I am interested in doing k-fold cross validation using the example given here. Is it possible? Thanks. 回答1: If you are using images with data generators, here's one way to do 10-fold cross-validation with Keras and scikit-learn. The strategy is to copy the files to training , validation , and test subfolders according to each fold. import numpy

问题 Running locally on a Jupyter notebook and using the MNIST dataset (28k entries, 28x28 pixels per image, the following takes 27 seconds . from sklearn.neighbors import KNeighborsClassifier knn_clf = KNeighborsClassifier(n_jobs=1) knn_clf.fit(pixels, labels) However, the following takes 1722 seconds , in other words ~64 times longer : from sklearn.model_selection import cross_val_predict y_train_pred = cross_val_predict(knn_clf, pixels, labels, cv = 3, n_jobs=1) My naive understanding is that