title: How to get accuracy in RandomForest Model in Python?
body: <p>I got this script, that predict with RandomForest and LinearRegression the values for the seconds dataset.That works ok, the accuracy for the linear regression is 18% , too bad.</p>
  
  <p>So Im trying with RandomForest, but I dont know how to calculate the accuracy of that model..</p>
  
  <pre><code>import pandas as pd
  
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.datasets import make_regression
  
  import numpy as np
  import pandas as pd
  import scipy
  import matplotlib.pyplot as plt
  from pylab import rcParams
  import urllib
  import sklearn
  from sklearn.linear_model import RidgeCV, LinearRegression, Lasso
  
  from sklearn.preprocessing import StandardScaler, PolynomialFeatures
  from sklearn.model_selection import GridSearchCV
  
  data = pd.read_csv('EncuestaVieja.csv')
  X = data[['Edad','Sexo','v1','v2','v3']]
  y = data['Alumna']
  
  dataP = pd.read_csv('EncuestaVieja_test.csv')
  X_p = dataP[['Edad','Sexo','v1','v2','v3']]
  y_p = dataP['Alumna']
  
  dataT = pd.read_csv('EncuestaVieja_test_2.csv')
  X_t = dataT[['Edad','Sexo','v1','v2','v3']]
  y_t = dataT['Alumna']
  regr = linear_model.LinearRegression()
  
  regr.fit(X, y)
  
  lr = RandomForestRegressor(n_estimators=50)
  lr.fit(X, y)
  
  X_test = pd.read_csv('EncuestaNueva.csv')[['Edad','Sexo','v1','v2','v3']]
  
  predictions = regr.predict(X_test)
  
  
  predictions2 = lr.predict(X_test)
  print( 'RandomForest Accuracy: ')
  print(((predictions2)))
  print( '')
  from sklearn.linear_model import LinearRegression
  regressor = LinearRegression()
  regressor.fit(X_p,y_p)
  accuracy = regressor.score(X_t,y_t)
  print( 'Linear Regression Accuracy: ', accuracy*100,'%')
  print(((predictions)))
  </code></pre>
  
  <p>OUTPUT:</p>
  
  <pre><code>RandomForest Accuracy: 
  [ 1.64  2.54  2.6   2.38  1.64  1.32  1.68  2.56  3.    2.28  2.38  2.68
    2.9   2.5   2.78  1.96  1.56  2.6   2.12  2.76  2.74  1.66  1.68  2.12
    2.3   2.36  2.28  2.28  2.82  1.7   1.86  2.36  1.24]
  
  Linear Regression Accuracy:  18.1336149086 %
  [ 1.2681851   1.02802219  3.13377072  2.96885127  2.30808853  1.98814349
    2.39233726  2.8638321   1.86640316  2.63073399  2.21166731  2.25201016
    1.95065189  2.65360517  3.08855254  1.01229733  2.18225606  2.41802534
    2.43539473  2.50227407  1.71105799  1.88238089  2.12152321  3.33525397
    2.72820453  2.43241713  2.88757874  2.6242382   2.63087916  1.98379487
    2.25430306  1.96810279  0.8554685 ]
  </code></pre>