1+ {
2+ "nbformat" : 4 ,
3+ "nbformat_minor" : 0 ,
4+ "metadata" : {
5+ "colab" : {
6+ "name" : " Decision Tree Regression"
7+ "provenance" : [],
8+ "toc_visible" : true ,
9+ "authorship_tag" : " ABX9TyOtWPwvzez0HBdURmm5Khw0"
10+ "include_colab_link" : true
11+ },
12+ "kernelspec" : {
13+ "name" : " python3"
14+ "display_name" : " Python 3"
15+ }
16+ },
17+ "cells" : [
18+ {
19+ "cell_type" : " markdown"
20+ "metadata" : {
21+ "id" : " view-in-github"
22+ "colab_type" : " text"
23+ },
24+ "source" : [
25+ " <a href=\" https://colab.research.google.com/github/Lakshaypahuja21/Machine-Learning-Basics/blob/master/Decision_Tree_Regression.ipynb\" target=\" _parent\" ><img src=\" https://colab.research.google.com/assets/colab-badge.svg\" alt=\" Open In Colab\" /></a>"
26+ ]
27+ },
28+ {
29+ "cell_type" : " markdown"
30+ "metadata" : {
31+ "id" : " 3CnrCTNoG0av"
32+ },
33+ "source" : [
34+ " # decision tree regression"
35+ ]
36+ },
37+ {
38+ "cell_type" : " markdown"
39+ "metadata" : {
40+ "id" : " 5pvfn-spG1NH"
41+ },
42+ "source" : [
43+ " ## lib\n "
44+ ]
45+ },
46+ {
47+ "cell_type" : " code"
48+ "metadata" : {
49+ "id" : " L-AxSbGLG7U8"
50+ },
51+ "source" : [
52+ " import numpy as np\n "
53+ " import pandas as pd\n "
54+ " import matplotlib.pyplot as plt\n "
55+ ],
56+ "execution_count" : 11 ,
57+ "outputs" : []
58+ },
59+ {
60+ "cell_type" : " markdown"
61+ "metadata" : {
62+ "id" : " M6xwaAc_HHYd"
63+ },
64+ "source" : [
65+ " ## dataset"
66+ ]
67+ },
68+ {
69+ "cell_type" : " code"
70+ "metadata" : {
71+ "id" : " FewyCAVzHIhp"
72+ },
73+ "source" : [
74+ " df = pd.read_csv('Position_Salaries.csv')\n "
75+ " x = df.iloc[:, 1:-1].values\n "
76+ " y = df.iloc[:, -1].values"
77+ ],
78+ "execution_count" : 12 ,
79+ "outputs" : []
80+ },
81+ {
82+ "cell_type" : " markdown"
83+ "metadata" : {
84+ "id" : " ySKUWzpYHKeY"
85+ },
86+ "source" : [
87+ " ## training the decision tree model on full data set"
88+ ]
89+ },
90+ {
91+ "cell_type" : " code"
92+ "metadata" : {
93+ "id" : " u1VfOSHDHOKL"
94+ "outputId" : " 95ee6b0e-fc0e-48c5-9ec2-1a6d62a3b79c"
95+ "colab" : {
96+ "base_uri" : " https://localhost:8080/"
97+ }
98+ },
99+ "source" : [
100+ " from sklearn.tree import DecisionTreeRegressor\n "
101+ " regressor = DecisionTreeRegressor(random_state = 0)\n "
102+ " regressor.fit(x, y)"
103+ ],
104+ "execution_count" : 13 ,
105+ "outputs" : [
106+ {
107+ "output_type" : " execute_result"
108+ "data" : {
109+ "text/plain" : [
110+ " DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n "
111+ " max_features=None, max_leaf_nodes=None,\n "
112+ " min_impurity_decrease=0.0, min_impurity_split=None,\n "
113+ " min_samples_leaf=1, min_samples_split=2,\n "
114+ " min_weight_fraction_leaf=0.0, presort='deprecated',\n "
115+ " random_state=0, splitter='best')"
116+ ]
117+ },
118+ "metadata" : {
119+ "tags" : []
120+ },
121+ "execution_count" : 13
122+ }
123+ ]
124+ },
125+ {
126+ "cell_type" : " markdown"
127+ "metadata" : {
128+ "id" : " sckY6hEpHU1l"
129+ },
130+ "source" : [
131+ " ## predicting new result"
132+ ]
133+ },
134+ {
135+ "cell_type" : " code"
136+ "metadata" : {
137+ "id" : " I-rs4m3OHWm0"
138+ "outputId" : " 22cffdf9-5b55-448e-aa02-ac91c20a9831"
139+ "colab" : {
140+ "base_uri" : " https://localhost:8080/"
141+ }
142+ },
143+ "source" : [
144+ " regressor.predict([[6.5]])"
145+ ],
146+ "execution_count" : 14 ,
147+ "outputs" : [
148+ {
149+ "output_type" : " execute_result"
150+ "data" : {
151+ "text/plain" : [
152+ " array([150000.])"
153+ ]
154+ },
155+ "metadata" : {
156+ "tags" : []
157+ },
158+ "execution_count" : 14
159+ }
160+ ]
161+ },
162+ {
163+ "cell_type" : " markdown"
164+ "metadata" : {
165+ "id" : " Soo32ta4HYAQ"
166+ },
167+ "source" : [
168+ " ## visualizing decision tree regression (high scale)"
169+ ]
170+ },
171+ {
172+ "cell_type" : " code"
173+ "metadata" : {
174+ "id" : " MOOolv7wdK5_"
175+ "outputId" : " 64efa57d-7850-46c1-8d0d-26fef667dc91"
176+ "colab" : {
177+ "base_uri" : " https://localhost:8080/"
178+ "height" : 295
179+ }
180+ },
181+ "source" : [
182+ " x_grid = np.arange(min(x), max(x), 0.1)\n "
183+ " x_grid = x_grid.reshape((len(x_grid), 1))\n "
184+ " plt.scatter(x,y, color = 'red')\n "
185+ " plt.plot(x_grid, regressor.predict(x_grid), color = 'blue')\n "
186+ " plt.title('truth or bluff(decision tree)')\n "
187+ " plt.xlabel('position level')\n "
188+ " plt.ylabel('salary')\n "
189+ " plt.show()"
190+ ],
191+ "execution_count" : 15 ,
192+ "outputs" : [
193+ {
194+ "output_type" : " display_data"
195+ "data" : {
196+ "image/png": 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\n",
197+ "text/plain" : [
198+ " <Figure size 432x288 with 1 Axes>"
199+ ]
200+ },
201+ "metadata" : {
202+ "tags" : [],
203+ "needs_background" : " light"
204+ }
205+ }
206+ ]
207+ },
208+ {
209+ "cell_type" : " markdown"
210+ "metadata" : {
211+ "id" : " NIdwA81eKapK"
212+ },
213+ "source" : [
214+ " ## not best for 2 variables|"
215+ ]
216+ }
217+ ]
218+ }
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