本文整理汇总了Python中sklearn.ensemble.ExtraTreesClassifier.max_features方法的典型用法代码示例。如果您正苦于以下问题：Python ExtraTreesClassifier.max_features方法的具体用法？Python ExtraTreesClassifier.max_features怎么用？Python ExtraTreesClassifier.max_features使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.ensemble.ExtraTreesClassifier的用法示例。
在下文中一共展示了ExtraTreesClassifier.max_features方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: range
# 需要导入模块: from sklearn.ensemble import ExtraTreesClassifier [as 别名]
# 或者: from sklearn.ensemble.ExtraTreesClassifier import max_features [as 别名]
f1.write('\nmodel type:'); f1.write(str(model_type))
f1.write('\nseed value: '); f1.write(str(seed_value))
f1.write('\nparameter grid \n'); f1.write(str(param_grid) )
f1.close()
# storage structure for forecasts
mvalid = np.zeros((xtrain.shape[0],len(param_grid)))
mfull = np.zeros((xtest.shape[0],len(param_grid)))
## build 2nd level forecasts
for i in range(len(param_grid)):
print "processing parameter combo:", i
# configure model with j-th combo of parameters
x = param_grid[i]
model.max_depth = int(x[0])
model.max_features = int(x[1])
model.max_features = int(x[2])
model.min_samples_leaf = int(x[3])
model.min_weight_fraction_leaf = x[4]
model.n_estimators = int(x[5])
# loop over folds
for j in range(0,n_folds):
idx0 = np.where(fold_index != j)
idx1 = np.where(fold_index == j)
x0 = np.array(xtrain)[idx0,:][0];
x1 = np.array(xtrain)[idx1,:][0]
y0 = np.array(y)[idx0];
y1 = np.array(y)[idx1]
model.fit(x0, y0)示例2: tuple
# 需要导入模块: from sklearn.ensemble import ExtraTreesClassifier [as 别名]
# 或者: from sklearn.ensemble.ExtraTreesClassifier import max_features [as 别名]
param_grid = tuple([n_vals, n_minleaf, n_minsplit, n_maxfeat])
param_grid = list(product(*param_grid))
# storage structure for forecasts
mvalid = np.zeros((xtrain.shape[0],len(param_grid)))
mfull = np.zeros((xtest.shape[0],len(param_grid)))
## build 2nd level forecasts
for i in range(len(param_grid)):
print "processing parameter combo:", i
# configure model with j-th combo of parameters
x = param_grid[i]
model.n_estimators = x[0]
model.min_samples_leaf = x[1]
model.min_samples_split = x[2]
model.max_features = x[3]
# loop over folds
for j in range(0,n_folds):
idx0 = np.where(fold_index != j)
idx1 = np.where(fold_index == j)
x0 = np.array(xtrain)[idx0,:][0];
x1 = np.array(xtrain)[idx1,:][0]
y0 = np.array(y_train)[idx0];
y1 = np.array(y_train)[idx1]
# fit the model on observations associated with subject whichSubject in this fold
model.fit(x0, y0)
mvalid[idx1,i] = model.predict_proba(x1)[:,1]
y_pre = model.predict_proba(x1)[:,1]
scores = roc_auc_score(y1,y_pre)