一、判断机器算法的性能1基本使用

1.目的

判断机器算法的性能（train test split）目的是帮助我们找到一个更好的模型/测试时数据，训练数据；预测的准确率（越接近1越准确）:
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2.使用pycharm函数封装

使用pycharm函数封装的形式运行train test split算法：

import numpy as np
def train_test_split(x,y,test_ratio=0.2,seed=None):"""将数据x和y按照test_ratio分割成训练数据集和测试数据集"""assert x.shape[0]==y.shape[0],\"the size of x must be equal to the sze of y"assert 0.0<=test_ratio<=1.0,\"test_ration must be valid"if seed:np.random.seed(seed)shuffle_indexes = np.random.permutation(len(x))test_size = int(len(x) * test_ratio)test_indexes = shuffle_indexes[:test_size]  # 确定测试数据集对应的索引train_indexes = shuffle_indexes[test_size:]  # 训练数据集所对应的索引x_train = x[train_indexes]y_train = y[train_indexes]x_test = x[test_indexes]y_test = y[test_indexes]return x_train,x_test,y_train,y_test

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3.sklearn中的train test split：

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4.完美调用：

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二、判断机器算法的性能2分类的准确度（accuracy）

分类的准确度（accuracy）：

准确度初步计算：

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完善KNNpy程序如下：

import numpy as np
from math import sqrt
from collections import Counter
from .metrics import accuracy_scoreclass KNNClassifier:def __init__(self, k):"""初始化kNN分类器"""assert k >= 1, "k must be valid"self.k = kself._X_train = Noneself._y_train = Nonedef fit(self, X_train, y_train):"""根据训练数据集X_train和y_train训练kNN分类器"""assert X_train.shape[0] == y_train.shape[0], \"the size of X_train must be equal to the size of y_train"assert self.k <= X_train.shape[0], \"the size of X_train must be at least k."self._X_train = X_trainself._y_train = y_trainreturn selfdef predict(self, X_predict):"""给定待预测数据集X_predict，返回表示X_predict的结果向量"""assert self._X_train is not None and self._y_train is not None, \"must fit before predict!"assert X_predict.shape[1] == self._X_train.shape[1], \"the feature number of X_predict must be equal to X_train"y_predict = [self._predict(x) for x in X_predict]return np.array(y_predict)def _predict(self, x):"""给定单个待预测数据x，返回x的预测结果值"""assert x.shape[0] == self._X_train.shape[1], \"the feature number of x must be equal to X_train"distances = [sqrt(np.sum((x_train - x) ** 2))for x_train in self._X_train]nearest = np.argsort(distances)topK_y = [self._y_train[i] for i in nearest[:self.k]]votes = Counter(topK_y)return votes.most_common(1)[0][0]def score(self, X_test, y_test):"""根据测试数据集 X_test 和 y_test 确定当前模型的准确度"""y_predict = self.predict(X_test)return accuracy_score(y_test, y_predict)def __repr__(self):return "KNN(k=%d)" % self.k