Interactive Confusion Matrix

In this tutorial, we’ll demonstrate how to plot an interactive confusion matrix using the penguins dataset.

import pandas as pd
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split

Load the dataset

df = sns.load_dataset("penguins")
df.dropna(inplace=True)
Y = df.species
Y = Y.map({"Adelie": 0, "Chinstrap": 1, "Gentoo": 2})
df.drop("species", inplace=True, axis=1)
se = pd.get_dummies(df["sex"], drop_first=True)
df = pd.concat([df, se], axis=1)
df.drop("sex", axis=1, inplace=True)
le = LabelEncoder()
df["island"] = le.fit_transform(df["island"])
X = df
X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.3, random_state=40
)
df.head()

	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	Male
0	2	39.1	18.7	181.0	3750.0	1
1	2	39.5	17.4	186.0	3800.0	0
2	2	40.3	18.0	195.0	3250.0	0
4	2	36.7	19.3	193.0	3450.0	0
5	2	39.3	20.6	190.0	3650.0	1

Train a model

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

Interactive confusion matrix

from sklearn_evaluation import plot

cm = plot.InteractiveConfusionMatrix.from_raw_data(
    y_test.tolist(),
    y_pred.tolist(),
    X_test=X_test,
    feature_subset=[
        "Male",
        "body_mass_g",
        "bill_depth_mm",
        "bill_length_mm",
        "flipper_length_mm",
    ],
    nsample=6,
)

Clicking on each quadrant displays two tables: Sample Observations and Quadrant Statistics. Sample Observations displays 5 random samples from the quadrant. Quadrant Statistics table displays some statistics on all the data that lies in this quadrant.

cm.chart