小苏打的学习博客

class sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(100, ), activation=’relu’, solver=’adam’, alpha=0.0001, batch_size=’auto’, learning_rate=’constant’, learning_rate_init=0.001, power_t=0...

sklearn.svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_s...

Sklearn.svm.LinearSVC(penalty=’l2’, loss=’squared_hinge’, dual=True, tol=0.0001, C=1.0, multi_class=’ovr’,fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_...

class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30,p=2, metric=’minkowski’, metric_params=None, n_jobs=1, **kwargs)

class sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_no...

目录自动切分手动切分K折交叉验证（k-fold cross validation）在训练深度学习模型的时候，通常将数据集切分为训练集和验证集。Keras提供了两种评估模型性能的方法：使用自动切分的验证集使用手动切分的验证集1、自动切分在Keras中，可以从数据集中切分出一部分作为验证集，并且在每次迭代(epoch)时在验证集中评估模型的性能。具体地，调用model.fit...