plot(train_sizes, test_scores_mean, 'o-', color="g", label="Validation") plt.xlabel("Training examples") plt.ylabel("scores") plt.legend(loc="best") plt.show() learning_curve函数中参数解释: estimator:表示我们所使用的的估计器 X:输入的feature y : 输入的target CV: 做训练集切割成...
scikitplot.estimators.plot_learning_curve生成不同训练样本下的训练和测试学习曲线图。 import scikitplot as skplt rf = RandomForestClassifier() skplt.estimators.plot_learning_curve(rf, X, y) plt.show() scikitplot.estimators.plot_feature_importances可视化特征重要性。 import scikitplot as skplt rf =...
import numpy as np from sklearn.learning_curve import learning_curve #c查看是否过拟合 def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=1, train_sizes=np.linspace(.05, 1., 20), verbose=0, plot=True): """ 画出data在某模型上的learning curve. 参数解释 --...
pyplot.plot(results['validation_1']['logloss'],label='test') # show the legend pyplot.legend() # show the plot pyplot.show() 就是这样。综合所有这些,下面列出了在综合分类任务上拟合XGBoost模型并绘制学习曲线的完整示例。 # plot learning curve of ...
myutil.plot_learning_curve(LinearRegression(),X,y,title) myutil.show_pic(title) 5.2 用make_regression数据(有噪音)进行线性回归 代码语言:javascript 代码运行次数:0 运行 AI代码解释 #加入噪音 def LinearRegression_for_make_regression_add_noise(): myutil = util() X,y = make_regression(n_samples...
ax.plot(abs_trains_sizes, test_scores_mean, label="Testing Accuracy", color="g") ax.fill_between(abs_trains_sizes, test_scores_mean- test_scores_std,test_scores_mean + test_scores_std, alpha=0.2, color="g") ax.set_title("Learning Curve with LinearSVC") ...
def plot_learning_curve(X, y, Xval, yval, l): """画出学习曲线""" m = X.shape[0] training_cost, cv_cost = [], [] for i in range(1,m+1): res = linear_regression(X[:i, :], y[:i], l) tc = costReg(res, X[:i, :], y[:i], 0) ...
plt.plot(train_sizes, train_loss_mean,'o-', color="r", label="Training") plt.plot(train_sizes, test_loss_mean,'o-', color="g", label="Cross-validation") plt.xlabel("Training examples") plt.ylabel("Loss") plt.legend(loc="best") ...
ridge=Ridge().fit(X_train,y_train)print('alpha=1,糖尿病数据训练集得分: {:.2%}'.format(ridge.score(X_train,y_train)))print('alpha=1,糖尿病数据测试集得分: {:.2%}'.format(ridge.score(X_test,y_test)))title="Ridge 糖尿病数据 alpha=1"myutil.plot_learning_curve(Ridge(),X,y,title...
plt.plot(np.sort(x),y_predict[np.argsort(x)],color='r') plt.show() 学习曲线 由学习曲线看欠拟合和过拟合,横轴代表训练用数据数量,纵轴为均方根误差 defplot_learning_curve(algo,X_train,X_test,y_train,y_test): train_score = []