Scikit-learn_模型评估与参数调优_估计起得分

一.描述

在Scikit-learn中，估计器是一个重要的角色，分类器和回归器都属于估计器，是机器学习算法的实现。score（）方法返回的是估计器得分也就是分类准确率

二.实例

• 使用准确性指标评价函数accuracy_score()

  from sklearn.datasets import load_iris
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.model_selection import train_test_split as tsplit
  from sklearn.metrics import accuracy_score
  X, y = load_iris(return_X_y=True)
  X_train, X_test, y_train, y_test = tsplit(X, y, test_size=0.1)
  knn = KNeighborsClassifier()
  knn.fit(X_train, y_train)
  y_pred = knn.predict(X_test)
  accuracy_score(y_test, y_pred)
  knn.score(X_test, y_test)
  

• 使用估价器的score()方法

  from sklearn.datasets import load_diabetes
  from sklearn.svm import SVR
  from sklearn.model_selection import train_test_split
  from sklearn import metrics
  X, y = load_iris(return_X_y=True)
  svr = SVR()
  svr.fit(X_train, y_train)
  y_pred = svr.predict(X_test)
  # 均方误差指标评价函数
  metrics.mean_squared_error(y_test, y_pred)
  # 中位数绝对误差指标评价函数
  metrics.median_absolute_error(y_test, y_pred)
  # 复相关系数指标评价函数
  metrics.r2_score(y_test, y_pred)
  # 直接使用估价器的score()方法
  svr.score(X_test, y_test)