During a regression task, I am getting low R^2 values, but elementwise difference between test set and prediction values is huge I am doing a random forest regression on my dataset (which has abut 15 input features and 1 target feature). I am getting a decently low R^2 of &lt;1 for both the train and test sets (please do let me know if &lt;1 is not a good-enough R^2 score).

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

# load dataset
df = pd.read_csv('Dataset.csv')

# split into input (X) and output (Y) variables
X = df.drop(['ID_COLUMN', 'TARGET_COLUMN'], axis=1)
Y = df.TARGET_COLUMN

# Split the data into 67% for training and 33% for testing
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33)

# Fitting the regression model to the dataset
regressor = RandomForestRegressor(n_estimators = 100, random_state = 50)
regressor.fit(X_train, Y_train.ravel()) # Using ravel() to avoid getting 'DataConversionWarning' warning message


print("Predicting Values:")
y_pred = regressor.predict(X_test)

print("Getting Model Performance...")

# Get regression scores
print("R^2 train = ", regressor.score(X_train, Y_train))
print("R^2 test = ", regressor.score(X_test, Y_test))


This outputs the following:

Predicting Values:
Getting Model Performance...
R^2 train =  0.9791000275450427
R^2 test = 0.8577464692386905


Then, I checked the difference between the actual target column values in the test dataset versus the predicted values, like so:

diff = []
for i in range(len(y_pred)):
    if Y_test.values[i]!=0: # a few values were 0 which was causing the corresponding diff value to become inf
        diff.append(100*np.abs(y_pred[i]-Y_test.values[i])/Y_test.values[i]) # element-wise percentage error


I found that the majority of the element-wise differences were between 40-60% and their mean was almost 50%!

np.mean(diff)
&gt;&gt;&gt; 49.07580695857447


So, which one is correct? Is the regression score correct and my model is good for this data, or is the element-wise error I calculated correct and the model didn't do well for this data? If its the latter, please advise on how to increase the prediction accuracy.



I also checked the rmse score:

import math
rmse = math.sqrt(np.mean((np.array(Y_test) - y_pred)**2))
rmse
&gt;&gt;&gt; 3.67328471827293


This seems quite high for the model to have done a good job, but please correct me if I'm wrong.

And I also checked the R^2 scores for different number of estimators:

import matplotlib.pyplot as plt
model = RandomForestRegressor(n_jobs=-1)
# Try different numbers of n_estimators
estimators = np.arange(10, 200, 10)
scores = []
for n in estimators:
    model.set_params(n_estimators=n)
    model.fit(X_train, Y_train)
    scores.append(model.score(X_test, Y_test))
plt.title("Effect of n_estimators")
plt.xlabel("n_estimator")
plt.ylabel("score")
plt.plot(estimators, scores)




Please advise.



I tried using linear regression first, and got a very high MSE (which is why I was trying out random forest):

from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

lr = LinearRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)

# The coefficients
print('Coefficients: \n', lr.coef_)
# The mean squared error
print("Mean squared error: %.2f" % mean_squared_error(y_test, y_pred))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % r2_score(y_test, y_pred))


Coefficients: 
 [ 1.93829229e-01 -4.68738825e-01  2.01635420e-01  6.35902010e-01
  6.57354434e-03  5.13180293e-03  2.84015810e-01 -1.31469084e-06
  1.95335035e+00]
Mean squared error: 86.92
Variance score: 0.08
This line looks wrong to me:

diff.append(100*np.abs(y_pred[i]-Y_test.values[i])/Y_test.values[i])


Shouldn't the abs be around the entire calculation?

diff.append(100*np.abs((y_pred[i]-Y_test.values[i])/Y_test.values[i]))


That aside, the RMSE calculation looks accurate and is in the scale of the error, and the $R^2$ is great, so all things being equal, I would lean towards looking for something you did wrong in assessing the errors.  That's why I was focused on your calculation.

One other thought, have you checked for outliers?  This could affect some measures and not others as drastically.