Python sklearn拆分訓練集、測試集及預測導出評分 決策樹

 

 

機器學習入門

　（注：無基礎可快速入門，想提高准確率還得多下功夫，文中各名詞不做過多解釋）

Python語言、pandas包、sklearn包   建議在Jupyter環境操作

操作步驟

1.pandas包加載給機器學習訓練的表格

　　依照機器學習領域的習慣，我們把特征叫做X，目標叫做y，通常一列數據最后一列作為目標列

2.映射數據列為整型(Python做決策樹需要整型或者實數)

3.拆分訓練集、測試集

4.sklearn創建訓練模型、測試模型准確率等

5.預測結果導出

算法

1.PCA算法
2.LDA算法
3.線性回歸
4.邏輯回歸
5.朴素貝葉斯
6.決策樹
7.SVM
8.神經網絡
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))

#處理轉換為整型（存在優化空間）
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)

#拆分訓練集、測試集
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)

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

#acc正確率
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)

#預測
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
## 模型訓練
#clf = SVC(kernel='linear')
#clf.fit(X_train, y_train)
## 模型存儲
#joblib.dump(clf, './model/svm_mode.pkl')
#