grid-search

问题 I was checking sklearn documentation webpage about GridSearchCV . One of attributes of GridSearchCV object is best_estimator_ . So here is my question. How to pass more than one estimator to GSCV object? Using a dictionary like: {'SVC()':{'C':10, 'gamma':0.01}, ' DecTreeClass()':{....}} ? 回答1: GridSearchCV works on parameters. It will train multiple estimators (but same class (one of SVC, or DecisionTreeClassifier, or other classifiers) with different parameter combinations from specified in

问题 Before scikit-learn 0.20 we could use result.grid_scores_[result.best_index_] to get the standard deviation. (It returned for exemple: mean: 0.76172, std: 0.05225, params: {'n_neighbors': 21} ) What's the best way in scikit-learn 0.20 to get the standard deviation of the best score ? 回答1: In newer versions, the grid_scores_ is renamed as cv_results_ . Following the documentation, you need this: best_index_ : int The index (of the cv_results_ arrays) which corresponds to the best > candidate

问题 I'm trying to use the log_loss argument in the scoring parameter of GridSearchCV to tune this multi-class (6 classes) classifier. I don't understand how to give it a label parameter. Even if I gave it sklearn.metrics.log_loss , it would change for each iteration in the cross-validation so I don't understand how to give it the labels parameter? I'm using Python v3.6 and Scikit-Learn v0.18.1 How can I use GridSearchCV with log_loss with multi-class model tuning? My class representation: 1 31 2

问题 I have a question about the cv parameter of sklearn's GridSearchCV. I'm working with data that has a time component to it, so I don't think random shuffling within KFold cross-validation seems sensible. Instead, I want to explicitly specify cutoffs for training, validation, and test data within a GridSearchCV . Can I do this? To better illuminate the question, here's how I would to that manually. import numpy as np import pandas as pd from sklearn.linear_model import Ridge np.random.seed(444)

问题 I'm trying to use custom cross-validation sets for a very specific dataset and scikit-optimize using BayesSearchCV . I've been able to replicate the error with scikit-learn using GridSearchCV . Straight from the documentation: cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: None, to use the default 3-fold cross validation, integer, to specify the number of folds in a (Stratified)KFold, An object to

问题 I am using ShuffleSplit to shuffle data, but I found there is an error TypeError Traceback (most recent call last) <ipython-input-36-192f7c286a58> in <module>() 1 # Fit the training data to the model using grid search ----> 2 reg = fit_model(X_train, y_train) 3 4 # Produce the value for 'max_depth' 5 print "Parameter 'max_depth' is {} for the optimal model.".format(reg.get_params()['max_depth']) <ipython-input-34-18b2799e585c> in fit_model(X, y) 32 33 # Fit the grid search object to the data

问题 I'm working on a learning to rank task, dataset has a column thread_id which is a group label (stratified data). In the evaluation phase I must take into account these groups as my scoring function works on a per-thread fashion (e.g. nDCG). Now, if I implement nDCG with a signature scorer(estimator, X, y) I can easily pass it to GridSearchCV as scoring function as in the example below: def my_nDCG(estimator, X, y): # group by X['thread_id'] # compute the result return result splitter =

问题 I am trying to solve a regression problem on Boston Dataset with help of random forest regressor.I was using GridSearchCV for selection of best hyperparameters. Problem 1 Should I fit the GridSearchCV on some X_train, y_train and then get the best parameters. OR Should I fit it on X, y to get best parameters.(X, y = entire dataset) Problem 2 Say If I fit it on X, y and get the best parameters and then build a new model on these best parameters. Now how should I train this new model on ?

问题 I am trying to use multiple feature columns in GridSearch with Pipeline. So I pass two columns for which I want to do a TfidfVectorizer, but I get into trouble when running the GridSearch. Xs = training_data.loc[:,['text','path_contents']] y = training_data['class_recoded'].astype('int32') for col in Xs: print Xs[col].shape print Xs.shape print y.shape # (2464L,) # (2464L,) # (2464, 2) # (2464L,) from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer

问题 I'm trying to find the best parameters for NN regression model using GridSearchCV with following code: param_grid = dict(optimizer=optimizer, epochs=epochs, batch_size=batches, init=init grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring='neg_mean_squared_error') grid_result = grid.fit(input_train, target_train) pred = grid.predict(input_test) As I understand, grid.predict(input_test) uses best parameters to predict the given input set. Is there any way to evaluate