From 684576ea86c8a5479e1b5dcf8b1c228c9eb2f1ed Mon Sep 17 00:00:00 2001
From: AnnaGe <40264376+AnnaXJGe@users.noreply.github.com>
Date: Sat, 11 Aug 2018 12:20:57 +0800
Subject: [PATCH] Delete Day 33 Random Forests

---
 Code/Day 33 Random Forests | 90 --------------------------------------
 1 file changed, 90 deletions(-)
 delete mode 100644 Code/Day 33 Random Forests

diff --git a/Code/Day 33 Random Forests b/Code/Day 33 Random Forests
deleted file mode 100644
index 190828b..0000000
--- a/Code/Day 33 Random Forests	
+++ /dev/null
@@ -1,90 +0,0 @@
-# 随机森林Random Forests
-<p align="center">
-  <img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%2033.png">
-</p>
-
-### 导入库
-```python
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-```
-
-### 导入数据集
-```python
-dataset = pd.read_csv('Social_Network_Ads.csv')
-X = dataset.iloc[:, [2, 3]].values
-y = dataset.iloc[:, 4].values
-```
-
-### 将数据集拆分成训练集和测试集
-```python
-from sklearn.cross_validation import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
-```
-
-### 特征缩放
-```python
-from sklearn.preprocessing import StandardScaler
-sc = StandardScaler()
-X_train = sc.fit_transform(X_train)
-X_test = sc.transform(X_test)
-```
-
-### 调试训练集的随机森林
-```python
-from sklearn.ensemble import RandomForestClassifier
-classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
-classifier.fit(X_train, y_train)
-```
-
-### 预测测试集结果
-```python
-y_pred = classifier.predict(X_test)
-```
-
-### 生成混淆矩阵，也称作误差矩阵
-```python
-from sklearn.metrics import confusion_matrix
-cm = confusion_matrix(y_test, y_pred)
-```
-
-### 将训练集结果可视化
-```python
-from matplotlib.colors import ListedColormap
-X_set, y_set = X_train, y_train
-X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
-                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
-plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
-             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
-plt.xlim(X1.min(), X1.max())
-plt.ylim(X2.min(), X2.max())
-for i, j in enumerate(np.unique(y_set)):
-    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
-                c = ListedColormap(('red', 'green'))(i), label = j)
-plt.title('Random Forest Classification (Training set)')
-plt.xlabel('Age')
-plt.ylabel('Estimated Salary')
-plt.legend()
-plt.show()
-```
-
-### 将数据集结果可视化
-```python
-from matplotlib.colors import ListedColormap
-X_set, y_set = X_test, y_test
-X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
-                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
-plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
-             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
-plt.xlim(X1.min(), X1.max())
-plt.ylim(X2.min(), X2.max())
-for i, j in enumerate(np.unique(y_set)):
-    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
-                c = ListedColormap(('red', 'green'))(i), label = j)
-plt.title('Random Forest Classification (Test set)')
-plt.xlabel('Age')
-plt.ylabel('Estimated Salary')
-plt.legend()
-plt.show()
-```