Answers for "how to calculate accuracy of linear regression model in r"

4

how to find the accuracy of linear regression model

# Simple Linear Regression
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Salary_Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].values
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 42)
# Fitting Simple Linear Regression to the Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
# Predicting the Test set results
y_pred = regressor.predict(X_test)
print('Coefficients: n', regressor.coef_)
# The mean squared error
print("Mean squared error: %.2f" % np.mean((regressor.predict(X_test) - y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % regressor.score(X_test, y_test))
Posted by: Guest on May-24-2020
0

classification accuracy metric in r

ClassificationAccuracyMetric <- function (Actual, Predicted){
  TP <- 0 # Actual normal point + Predicted normal point
  FN <- 0 # Actual normal point + Predicted fault point
  FP <- 0 # Actual fault point + predicted normal point
  TN <- 0 # Actual fault point + Predicted fault point
  for (i in 1:length(Actual)){
    if (Actual[i]==0 & Predicted[i]==0){
      TP=TP+1
    }
    if (Actual[i]==0 & Predicted[i]==1){
      FN=FN+1
    }
    if (Actual[i]==1 & Predicted[i]==0){
      FP=FP+1
    }
    if (Actual[i]==1 & Predicted[i]==1){
      TN=TN+1
    }
  }

  return ((TN + TP)/(TN + TP + FN + FP))
}

print(ClassificationAccuracyMetric( c(1,1,0,0,1,0,1,1,1,1), c(1,1,0,0,1,0,1,1,1,0) ))
Posted by: Guest on October-20-2020

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