Question: I am training a model with GridSearchCV in order to find the best parameters Code: grid_params = { 'n_estimators': [100, 200, 300, 400], 'criterion': ['gini', 'entropy'], 'max_features': ['auto', 'sqrt', 'log2'] } gs = GridSearchCV( RandomForestClassifier(), grid_params, cv=2, verbose=1, n_jobs=-1 ) clf = gs.fit(X_train, y_train)  This is a slow process, and after this, I print the confusion matrix, but I want to plot the validation curve to in order to check if there are overfitting for this I use the following code: train_scores, valid_scores = validation_curve(clf.best_estimator_, X, y)  The problem is that I need to set param_name, param_range, but I don't want to train again, because it is a too slow process. Another choice is to use gs, instead of clf.best_estimator_, but I need the gs trained, in order to get another information. How can I plot the validation curve, and keep the gs trainer, without train two times?

Answer: You can use the cv_results_ attribute of GridSearchCV and get the results for each combination of hyperparameters. Validation Curve is meant to depict the impact of single parameter in training and cross validation scores. Since fine tuning is done for multiple parameters in GridSearchCV, multiple plots are required to vizualise the impact of each parameter. Point to note is that the other parameter's impact has to be averaged out. This can be achieved by doing groupby on each parameter separately. For mean train and test - the mean of means would work out, but for standard deviation we have to use pooled variance because std deviations for each combination in Cross validation is almost constant. from sklearn.datasets import make_classification from sklearn.model_selection import GridSearchCV, train_test_split from sklearn.ensemble import RandomForestClassifier import matplotlib.pyplot as plt import numpy as np X, y = make_classification(n_samples=1000, n_features=100, n_informative=2, class_sep=0.1,random_state=7) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) grid_params = { 'n_estimators': [10, 20], 'max_features': ['sqrt', 'log2'], 'criterion': ['gini', 'entropy'], 'max_depth': [2, 5,] } cv = 5 gs = GridSearchCV( RandomForestClassifier(random_state=42), grid_params, cv=cv, verbose=1, n_jobs=-1, return_train_score=True # set this for train score ) gs.fit(X_train, y_train) import pandas as pd df = pd.DataFrame(gs.cv_results_) results = ['mean_test_score', 'mean_train_score', 'std_test_score', 'std_train_score'] # https://en.wikipedia.org/wiki/Pooled_variance#Pooled_standard_deviation def pooled_var(stds): n = cv # size of each group return np.sqrt(sum((n-1)*(stds**2))/ len(stds)*(n-1)) fig, axes = plt.subplots(1, len(grid_params), figsize = (5*len(grid_params), 7), sharey='row') axes[0].set_ylabel("Score", fontsize=25) lw = 2 for idx, (param_name, param_range) in enumerate(grid_params.items()): grouped_df = df.groupby(f'param_{param_name}')[results]\ .agg({'mean_train_score': 'mean', 'mean_test_score': 'mean', 'std_train_score': pooled_var, 'std_test_score': pooled_var}) previous_group = df.groupby(f'param_{param_name}')[results] axes[idx].set_xlabel(param_name, fontsize=30) axes[idx].set_ylim(0.0, 1.1) axes[idx].plot(param_range, grouped_df['mean_train_score'], label="Training score", color="darkorange", lw=lw) axes[idx].fill_between(param_range, grouped_df['mean_train_score'] - grouped_df['std_train_score'], grouped_df['mean_train_score'] + grouped_df['std_train_score'], alpha=0.2, color="darkorange", lw=lw) axes[idx].plot(param_range, grouped_df['mean_test_score'], label="Cross-validation score", color="navy", lw=lw) axes[idx].fill_between(param_range, grouped_df['mean_test_score'] - grouped_df['std_test_score'], grouped_df['mean_test_score'] + grouped_df['std_test_score'], alpha=0.2, color="navy", lw=lw) handles, labels = axes[0].get_legend_handles_labels() fig.suptitle('Validation curves', fontsize=40) fig.legend(handles, labels, loc=8, ncol=2, fontsize=20) fig.subplots_adjust(bottom=0.25, top=0.85) plt.show()   Note: line plots are not the right one for parameters with string values like criterion, you could modify it to be bar plots with error bars.