5. max_iter:迭代的最大次数,默认为1000。 6. tol:迭代的停止阈值,默认为1e-3。 7. shuffle:是否在每次迭代之前打乱样本,默认为True。 8. random_state:随机种子,默认为None。 9. learning_rate:学习率的类型,默认为"invscaling"。也可以选择"constant"和"optimal"等其他学习率。 10. eta0:学习率的初始...
2.6 max_iter •类型:整数 •默认值:1000 该参数指定了随机梯度下降算法的最大迭代次数。模型在达到最大迭代次数之后会停止训练。 2.7 tol •类型:浮点数 •默认值:1e-3 该参数用于控制算法的收敛性。当模型的损失函数变化小于tol时,算法将停止迭代。 2.8 shuffle •类型:布尔值 •默认值:True 该参...
defbuild_sgd_regressor(X_test, X_train_full, y_train_full):#print "Building SGD regressor..."rf =SGDRegressor(loss="modified_huber", penalty="elasticnet", n_iter=20000, alpha=0.1, epsilon=0.01) probas_rf = rf.fit(X_train_full, y_train_full).predict(X_test)returnprobas_rf 开发者...
if you use max_iter=5 (not 20) it will be faster and maybe it's good enough for good predictions? not there what to expect here. Add a remark to the doc? … Author tirthajyoti commented Sep 5, 2019 Current documentation on this gives a clear (and perhaps wrong) impression that as...
total += y.shape[0]if(n_batch+1)%1000==0:ifregression:#y_pred_validate_val = clf.decision_function(X_validate)y_pred_validate_val = clf.predict(X_validate)else:#y_pred_validate_val = clf.decision_function(X_validate)y_pred_validate_val = clf.predict_proba(X_validate)[:,1]print'...
a.max_iter:最大迭代次数,默认为1000。限制迭代次数可以防止模型在局部极小值处过度拟合。 b.tol: Tolerance for the stopping criteria.The training will stop when both the loss on the validation set and the absolute improvement of the loss over the previous iteration are both below this value.Defaul...