# Encoding with the Weight of Evidence

The **Weight of Evidence** (**WoE**) was developed primarily for credit and financial industries to facilitate variable screening and exploratory analysis and to build more predictive linear models to evaluate the risk of loan defaults.

The WoE is computed from the basic odds ratio:

Here, positive and negative refer to the values of the target being 1 or 0, respectively. The proportion of positive cases per category is determined as the sum of positive cases per category group divided by the total positive cases in the training set, and the proportion of negative cases per category is determined as the sum of negative cases per category group divided by the total number of negative observations in the training set.

The WoE has the following characteristics:

- WoE = 0 if p(positive) / p(negative) = 1; that is, if the outcome is random
- WoE > 0 if p(positive) > p(negative)
- WoE < 0 if p(negative) > p(positive)

This allows us to directly visualize the predictive power of the category in the variable: the higher the WoE, the more likely the event will occur. If the WoE is positive, the event is likely to occur:

Logistic regression models a binary response, *Y*, based on *X* predictor variables, assuming that there is a linear relationship between *X* and the log of odds of *Y*.

Here, *log (p(Y=1)/p(Y=0))* is the log of odds. As you can see, the WoE encodes the categories in the same scale – that is, the log of odds – as the outcome of the logistic regression.

Therefore, by using WoE, the predictors are prepared and coded on the same scale, and the parameters in the logistic regression model – that is, the coefficients – can be directly compared.

In this recipe, we will perform WoE encoding using pandas and Feature-engine.

## How to do it...

Let’s begin by making some imports and preparing the data:

- Import the required libraries and functions:
import numpy as np import pandas as pd from sklearn.model_selection import train_test_split

- Let’s load the dataset and divide it into train and test sets:
data = pd.read_csv("credit_approval_uci.csv") X_train, X_test, y_train, y_test = train_test_split( data.drop(labels=["target"], axis=1), data["target"], test_size=0.3, random_state=0, )

- Let’s get the inverse of the target values to be able to calculate the negative cases:
neg_y_train = pd.Series( np.where(y_train == 1, 0, 1), index=y_train.index )

- Let’s determine the number of observations where the target variable takes a value of 1 or 0:
total_pos = y_train.sum() total_neg = neg_y_train.sum()

- Now, let’s calculate the numerator and denominator of the WoE’s formula, which we discussed earlier in this recipe:
pos = y_train.groupby( X_train["A1"]).sum() / total_pos neg = neg_y_train.groupby( X_train["A1"]).sum() / total_neg

- Now, let’s calculate the WoE per category:
woe = np.log(pos/neg)

We can display the series with the category to WoE pairs by executing `print(woe)`

:

A1 Missing 0.203599 a 0.092373 b -0.042410 dtype: float64

- Finally, let’s replace the categories of
`A1`

with the WoE:X_train["A1"] = X_train["A1"].map(woe) X_test["A1"] = X_test["A1"].map(woe)

You can inspect the encoded variable by executing `X_train["A1"].head()`

.

Now, let’s perform WoE encoding using Feature-engine. First, we need to separate the data into train and test sets, as we did in *step 2*.

- Let’s import the encoder:
from feature_engine.encoding import WoEEncoder

- Next, let’s set up the encoder so that we can encode three categorical variables:
woe_enc = WoEEncoder(variables = ["A1", "A9", "A12"])

Tip

Feature-engine’s `WoEEncoder()`

will return an error if `p(0)=0`

for any category because the division by `0`

is not defined. To avoid this error, we can group infrequent categories, as we will discuss in the next recipe, *Grouping rare or **infrequent categories*.

- Let’s fit the transformer to the train set so that it learns and stores the WoE of the different categories:
woe_enc.fit(X_train, y_train)

Tip

We can display the dictionaries with the categories to WoE pairs by executing `woe_enc.encoder_dict_`

.

- Finally, let’s encode the three categorical variables in the train and test sets:
X_train_enc = woe_enc.transform(X_train) X_test_enc = woe_enc.transform(X_test)

Feature-engine returns pandas DataFrames containing the encoded categorical variables ready to use in machine learning models.

## How it works...

First, with pandas `sum()`

, we determined the total number of positive and negative cases. Next, using pandas `groupby()`

, we determined the fraction of positive and negative cases per category. And with that, we calculated the WoE per category.

Finally, we automated the procedure with Feature-engine. We used `WoEEncoder()`

, which learned the WoE per category with the `fit()`

method, and then used `transform()`

, which replaced the categories with the corresponding numbers.

## See also

For an implementation of WoE with Category Encoders, visit this book’s GitHub repository.