不知道为什么最近突然觉得牛客网很火，好奇心驱使下我也点开看了看…发现真的不错。
机器学习是python新增加的板块，其实只有5道题哈哈。
ps:题目很简单很基础，真的很适合刚刚入门机器学习的小白检验阶段性的学习成果。
趁着题还很少，将来出一道做一道，岂不是成就感满满。
鸢尾花分类_1
原题：鸢尾花分类_1_牛客题霸_牛客网 (nowcoder.com)
这道题采用贝叶斯算法能够保证该数据集下准确率在100%。
# 朴素贝叶斯from sklearn import datasetsfrom sklearn.model_selection import train_test_splitfrom sklearn.naive_bayes import GaussianNBfrom sklearn.metrics import accuracy_scoredef train_and_predict(train_input_features, train_outputs, prediction_features): G = GaussianNB() G.fit(train_input_features, train_outputs) y_pred = G.predict(prediction_features) return y_prediris = datasets.load_iris()X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=0)y_pred = train_and_predict(X_train, y_train, X_test)if y_pred is not None: print(accuracy_score(y_pred,y_test))鸢尾花分类_2
原题：鸢尾花分类_2_牛客题霸_牛客网 (nowcoder.com)
我使用的是决策树模型，默认参数下该二分类问题准确率还是100%
import numpy as npfrom sklearn import datasetsfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_scorefrom sklearn.tree import DecisionTreeClassifierdef transform_three2two_cate(): data = datasets.load_iris() new_data = np.hstack([data.data, data.target[:, np.newaxis]]) new_feat = new_data[new_data[:, -1] != 2][:, :4] new_label = new_data[new_data[:, -1] != 2][:, -1] return new_feat, new_labeldef train_and_evaluate(): data_X, data_Y = transform_three2two_cate() train_x, test_x, train_y, test_y = train_test_split(data_X, data_Y, test_size=0.2) DT = DecisionTreeClassifier() DT.fit(train_x, train_y) y_predict = DT.predict(test_x) print(accuracy_score(y_predict, test_y))if __name__ == "__main__": train_and_evaluate()信息熵的计算
原题：决策树的生成与训练-信息熵的计算_牛客题霸_牛客网 (nowcoder.com)
这道题十分简单，我的做法是把下面的数据转换为numpy的ndarray矩阵取出最后一列，直接套公式：
import numpy as npimport pandas as pdfrom collections import CounterdataSet = pd.read_csv('dataSet.csv', header=None).values[:, -1]def calcInfoEnt(dataSet): numEntres = len(dataSet) cnt = Counter(dataSet) # 计数每个值出现的次数 probability_lst = [1.0 * cnt[i] / numEntres for i in cnt] return -np.sum([p * np.log2(p) for p in probability_lst])if __name__ == '__main__': print(calcInfoEnt(dataSet))信息增益的计算
原题：决策树的生成与训练-信息增益_牛客题霸_牛客网 (nowcoder.com)
import numpy as npimport pandas as pdfrom collections import Counterimport randomdataSet = pd.read_csv('dataSet.csv', header=None).values.T # 转置 5*15数组def entropy(data): # data 一维数组 numEntres = len(data) cnt = Counter(data) # 计数每个值出现的次数 Counter({1: 8, 0: 5}) probability_lst = [1.0 * cnt[i] / numEntres for i in cnt] return -np.sum([p * np.log2(p) for p in probability_lst]) # 返回信息熵def calc_max_info_gain(dataSet): label = np.array(dataSet[-1]) total_entropy = entropy(label) max_info_gain = [0, 0] for feature in range(4): # 4种特征 我命名为特征：0 1 2 3 f_index = {} for idx, v in enumerate(dataSet[feature]): if v not in f_index: f_index[v] = [] f_index[v].append(idx) f_impurity = 0 for k in f_index: # 根据该特征取值对应的数组下标 取出对应的标签列表 比如分支1有多少个正负例 分支2有... f_l = label[f_index[k]] f_impurity += entropy(f_l) * len(f_l) / len(label) # 循环结束得到各分支混杂度的期望 gain = total_entropy - f_impurity # 信息增益IG if gain > max_info_gain[1]: max_info_gain = [feature, gain] return max_info_gainif __name__ == '__main__': info_res = calc_max_info_gain(dataSet) print("信息增益最大的特征索引为：{0},对应的信息增益为{1}".format(info_res[0], info_res[1]))使用梯度下降对逻辑回归进行训练
原题：使用梯度下降对逻辑回归进行训练_牛客题霸_牛客网 (nowcoder.com)
import numpy as npimport pandas as pddef generate_data(): datasets = pd.read_csv('dataSet.csv', header=None).values.tolist() labels = pd.read_csv('labels.csv', header=None).values.tolist() return datasets, labelsdef sigmoid(X): hx = 1/(1+np.exp(-X)) return hx #code end heredef gradientDescent(dataMatIn, classLabels): alpha = 0.001 # 学习率，也就是题目描述中的 α iteration_nums = 100 # 迭代次数，也就是for循环的次数 dataMatrix = np.mat(dataMatIn) labelMat = np.mat(classLabels).transpose() m, n = np.shape(dataMatrix) # 返回dataMatrix的大小。m为行数,n为列数。 weight_mat = np.ones((n, 1)) #初始化权重矩阵 for i in range(iteration_nums): hx=sigmoid(dataMatrix*weight_mat) weight_mat-=alpha*dataMatrix.transpose()*(hx-labelMat) return weight_matif __name__ == '__main__': dataMat, labelMat = generate_data() print(gradientDescent(dataMat, labelMat))