There are 3 different APIs for model evaluation:

1. **Estimator score method**: Estimator/model object has a ‘score()’ method that provides a default evaluation

2. **Scoring parameter**: Predefined scoring parameter that can be passed into cross_val_score() method

3. **Metric function**: Functions defined in the metrics module

Mean Squared Error is an example of **Scoring parameter** API.

– Similar to Mean Absolute Error where the error is magnified using the square function

– To get the original unit, you can take square root also known as Root Mean Squared Error (or RMSE)

**Caveat:** **cross_val_score()** reports scores in ascending order (largest score is best). But **MSE** is naturally descending scores (the smallest score is best). Thus we need to use **‘neg_mean_squared_error’** to invert the sorting. This also results in the score to be negative even though the value can never be negative.

**Note**:

– For the regression problem, we will use the Boston house price dataset.

– Estimator/Algorithm: Linear Regression

– Cross-Validation Split: K-Fold (k=10)

This **recipe** includes the following topics:

- Load data/file from github
- Split columns into the usual feature columns(X) and target column(Y)
- Set k-fold count to 10
- Set seed to reproduce the same random data each time
- Split data using
**KFold()**class - Instantiate a regression model
**(LinearRegression)** - Set scoring parameter to
**‘neg_mean_squared_error’** - Call
**cross_val_score()**to run cross validation - Calculate mean and standard deviation from scores returned by cross_val_score()

```
# import modules
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
# read data file from github
# dataframe: houseDf
gitFileURL = 'https://raw.githubusercontent.com/andrewgurung/data-repository/master/housing.csv'
cols = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
houseDf = pd.read_csv(gitFileURL, delim_whitespace=True, names = cols)
# convert into numpy array for scikit-learn
houseArr = houseDf.values
# Let's split columns into the usual feature columns(X) and target column(Y)
# Y represents the target 'MEDV' column
X = houseArr[:, 0:13]
Y = houseArr[:, 13]
# set k-fold count
folds = 10
# set seed to reproduce the same random data each time
seed = 7
# split data using KFold
kfold = KFold(n_splits=folds, random_state=seed)
# instantiate a regression model
model = LinearRegression()
# set scoring parameter to 'neg_mean_squared_error'
scoring = 'neg_mean_squared_error'
# call cross_val_score() to run cross validation
resultArr = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
# calculate mean of scores for all folds
mse = resultArr.mean()
# calculate standard deviation
stdAccuracy = resultArr.std()
# display Mean Squared Error
# descending score(smallest score is best) is denoted by negative even though the value is positive
print("Mean Absolute Error: %.3f, Standard Deviation : %.3f" % (mse, stdAccuracy))
```

```
Mean Absolute Error: -34.705, Standard Deviation : 45.574
```