cross-validation

问题 I am implementing a neural network and I would like to assess its performance with cross validation. Here is my current code: def recall_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + K.epsilon()) return recall def precision_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred

问题 I am implementing a neural network and I would like to assess its performance with cross validation. Here is my current code: def recall_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + K.epsilon()) return recall def precision_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred

问题 I am implementing a neural network and I would like to assess its performance with cross validation. Here is my current code: def recall_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + K.epsilon()) return recall def precision_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred

问题 I would like to cross-validate a neural network using the package neuralnet and caret . The data df can be copied from this post. When running the neuralnet() function, there is an argument called hidden where you can set the hidden layers and neurons in each. Let's say I want 2 hidden layers with 3 and 2 neurons respectively. It would be written as hidden = c(3, 2) . However, as I want to cross-validate it, I decided to use the fantastic caret package. But when using the function train() , I

问题 I would like to cross-validate a neural network using the package neuralnet and caret . The data df can be copied from this post. When running the neuralnet() function, there is an argument called hidden where you can set the hidden layers and neurons in each. Let's say I want 2 hidden layers with 3 and 2 neurons respectively. It would be written as hidden = c(3, 2) . However, as I want to cross-validate it, I decided to use the fantastic caret package. But when using the function train() , I

问题 I would like to cross-validate a neural network using the package neuralnet and caret . The data df can be copied from this post. When running the neuralnet() function, there is an argument called hidden where you can set the hidden layers and neurons in each. Let's say I want 2 hidden layers with 3 and 2 neurons respectively. It would be written as hidden = c(3, 2) . However, as I want to cross-validate it, I decided to use the fantastic caret package. But when using the function train() , I

问题 This question already has an answer here : Retrieving specific classifiers and data from GridSearchCV (1 answer) Closed 3 days ago . GridSearchCV and RandomizedSearchCV has best_estimator_ that : Returns only the best estimator/model Find the best estimator via one of the simple scoring methods : accuracy, recall, precision, etc. Evaluate based on training sets only I would like to enrich those limitations with My own definition of scoring methods Evaluate further on test set rather than

问题 Recently, I am doing multiple experiments to compare Python XgBoost and LightGBM. It seems that this LightGBM is a new algorithm that people say it works better than XGBoost in both speed and accuracy. This is LightGBM GitHub. This is LightGBM python API documents, here you will find python functions you can call. It can be directly called from LightGBM model and also can be called by LightGBM scikit-learn. This is the XGBoost Python API I use. As you can see, it has very similar data

问题 I am trying to split my dataset into training and testing dataset, but I am getting this error: X_train,X_test,Y_train,Y_test = sklearn.cross_validation.train_test_split(X,df1['ENTRIESn_hourly']) AttributeError Traceback (most recent call last) <ipython-input-53-5445dab94861> in <module>() ----> 1 X_train,X_test,Y_train,Y_test = sklearn.cross_validation.train_test_split(X,df1['ENTRIESn_hourly']) AttributeError: module 'sklearn' has no attribute 'cross_validation' How can I handle this? 回答1:

问题 For the given imbalanced data , I have created a different pipelines for standardization & one hot encoding numeric_transformer = Pipeline(steps = [('scaler', StandardScaler())]) categorical_transformer = Pipeline(steps=['ohe', OneHotCategoricalEncoder()]) After that a column transformer keeping the above pipelines in one from sklearn.compose import ColumnTransformer preprocessor = ColumnTransformer( transformers=[ ('num', numeric_transformer, numeric_features), ('cat', categorical