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

Logarithmic Loss is an example of **Scoring parameter** API.

Smaller logloss is better with 0 representing a perfect logloss.

**Note**:

– For the classification problem, we will use the Pima Indians onset of diabetes dataset.

– Estimator/Algorithm: **Logistic 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 classification model
**(LogisticRegression)** - Set scoring parameter to ‘
**neg_log_loss**‘ - 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 LogisticRegression
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
# read data file from github
# dataframe: pimaDf
gitFileURL = 'https://raw.githubusercontent.com/andrewgurung/data-repository/master/pima-indians-diabetes.data.csv'
cols = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
pimaDf = pd.read_csv(gitFileURL, names = cols)
# convert into numpy array for scikit-learn
pimaArr = pimaDf.values
# Let's split columns into the usual feature columns(X) and target column(Y)
# Y represents the target 'class' column whose value is either '0' or '1'
X = pimaArr[:, 0:8]
Y = pimaArr[:, 8]
# 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 classification model
model = LogisticRegression()
# set scoring parameter to 'neg_log_loss'
scoring = 'neg_log_loss'
# 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
meanAccuracy = resultArr.mean() * 100
# calculate standard deviation of scores for all folds
stdAccuracy = resultArr.std() * 100
# display accuracy
# descending score(smallest score is best) is denoted by negative even though the value is positive
print("Mean accuracy: %.3f%%, Standard deviation: %.3f%%" % (meanAccuracy, stdAccuracy))
```

```
Mean accuracy: -49.252%, Standard deviation: 4.707%
```