bias variance

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pandas import read_csv
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from mlxtend.evaluate import bias_variance_decomp

dataframe = read_csv("housing.csv")

data = dataframe.values
X, y = data[:, :-1], data[:, -1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)

model = LinearRegression()

mse, bias, var = bias_variance_decomp(model, X_train, y_train, X_test, y_test, loss='mse', num_rounds=200, random_seed=1)

print('MSE: %.3f' % mse)
print('Bias: %.3f' % bias)
print('Variance: %.3f' % var)