Python sklearn拆分训练集、测试集及预测导出评分 决策树

 

 

机器学习入门

　（注：无基础可快速入门，想提高准确率还得多下功夫，文中各名词不做过多解释）

Python语言、pandas包、sklearn包   建议在Jupyter环境操作

操作步骤

1.pandas包加载给机器学习训练的表格

　　依照机器学习领域的习惯，我们把特征叫做X，目标叫做y，通常一列数据最后一列作为目标列

2.映射数据列为整型(Python做决策树需要整型或者实数)

3.拆分训练集、测试集

4.sklearn创建训练模型、测试模型准确率等

5.预测结果导出

算法

1.PCA算法
2.LDA算法
3.线性回归
4.逻辑回归
5.朴素贝叶斯
6.决策树
7.SVM
8.神经网络
9.KNN算法

import pandas as pd
import matplotlib.pyplot as plt 
X = pd.read_csv('x_train.csv')

X = X.drop('target', axis=1)
y = df.target

#print(X.shape,y.head(10),y.shape,y.head(10))

#处理转换为整型（存在优化空间）
from sklearn.preprocessing import LabelEncoder
from collections import defaultdict
d = defaultdict(LabelEncoder)
X_train = X.apply(lambda x: d[x.name].fit_transform(x))

#X_train.tail(10)

#拆分训练集、测试集
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_train, y,test_size=0.25, random_state=7)
#print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

#决策树
from sklearn import tree
clf = tree.DecisionTreeClassifier(max_depth=3)
clf = clf.fit(X_train, y_train)

#acc正确率
from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, clf.predict(X_test)))

#F1 score
#from sklearn import metrics
#predict_labels = clf.predict(X_test)
#F1_scores = metrics.f1_score(y_test, predict_labels, pos_label=0)
#print(F1_scores)

#预测
X_pred = pd.read_csv('x_test')

dx = defaultdict(LabelEncoder)
X_pred = X_pred.apply(lambda x: dx[x.name].fit_transform(x))

pred_list = clf.predict(X_pred)
pred_proba_list = clf.predict_proba(X_pred)
print(pred_list)
print(pred_proba_list)
print(type(pred_list),type(pred_proba_list))

tag_list =pred_list.tolist()
proba_list = []
for i in pred_proba_list.tolist():
    proba_list.append(i[1])



X_pred["Proba"] = proba_list
X_pred["Tag"] = tag_list

X_pred.head(10)
X_pred.to_csv('./predict.csv',index=False,encoding='utf-8')


#from sklearn.svm import SVC
## 模型训练
#clf = SVC(kernel='linear')
#clf.fit(X_train, y_train)
## 模型存储
#joblib.dump(clf, './model/svm_mode.pkl')
#