Partial Dependence Plots

Another tool that is model-agnostic is a partial dependence plot. It is a visual tool for analyzing the effect of a feature on the target variable. To achieve this, we can plot the values of the feature we are interested in analyzing on the x-axis and the target variable on the y-axis and then show all the observations from the dataset on this graph. Let's try it on the Breast Cancer dataset from sklearn:

from sklearn.datasets import load_breast_cancer
import pandas as pd
data = load_breast_cancer()
df = pd.DataFrame(data.data, columns=data.feature_names)

Now that we have loaded the data and converted it to a DataFrame, let's have a look at the worst concave points column:

import altair as alt
alt.Chart(df).mark_circle(size=60).encode(
    x='worst concave points',
    y='target'
)

Figure 9.28: Scatter plot of the worst concave points and target variables

From this plot, we can see:

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