Scikit-Learn Classifier Comparison

Introduction

In this lab, we will compare several classifiers in Scikit-Learn on synthetic datasets. The purpose of this lab is to illustrate the nature of decision boundaries of different classifiers. We will preprocess the datasets, split them into training and test parts, and plot the datasets. We will then iterate over classifiers, fit the classifiers on the training data, plot the decision boundaries, and plot the testing data. Finally, we will display the classification accuracy on the test set.

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Skills Graph

Import necessary libraries

We will start by importing the necessary libraries.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.inspection import DecisionBoundaryDisplay

Prepare the datasets

We will use three synthetic datasets: moons, circles, and linearly separable. We will preprocess each dataset by splitting them into training and test parts, and then plot the datasets.

## Prepare datasets
X, y = make_classification(
    n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1
)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)

datasets = [
    make_moons(noise=0.3, random_state=0),
    make_circles(noise=0.2, factor=0.5, random_state=1),
    linearly_separable,
]

## Plot datasets
figure = plt.figure(figsize=(27, 9))
i = 1
for ds_cnt, ds in enumerate(datasets):
    X, y = ds
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.4, random_state=42
    )

    x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
    y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5

    cm = plt.cm.RdBu
    cm_bright = ListedColormap(["#FF0000", "#0000FF"])
    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
    if ds_cnt == 0:
        ax.set_title("Input data")
    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
    ax.scatter(
        X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k"
    )
    ax.set_xlim(x_min, x_max)
    ax.set_ylim(y_min, y_max)
    ax.set_xticks(())
    ax.set_yticks(())
    i += 1

Compare classifiers

We will iterate over classifiers, fit the classifiers on the training data, plot the decision boundaries, and plot the testing data. We will also display the classification accuracy on the test set.

## Define classifiers
names = [
    "Nearest Neighbors",
    "Linear SVM",
    "RBF SVM",
    "Gaussian Process",
    "Decision Tree",
    "Random Forest",
    "Neural Net",
    "AdaBoost",
    "Naive Bayes",
    "QDA",
]

classifiers = [
    KNeighborsClassifier(3),
    SVC(kernel="linear", C=0.025),
    SVC(gamma=2, C=1),
    GaussianProcessClassifier(1.0 * RBF(1.0)),
    DecisionTreeClassifier(max_depth=5),
    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
    MLPClassifier(alpha=1, max_iter=1000),
    AdaBoostClassifier(),
    GaussianNB(),
    QuadraticDiscriminantAnalysis(),
]

## Compare classifiers
for name, clf in zip(names, classifiers):
    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)

    clf = make_pipeline(StandardScaler(), clf)
    clf.fit(X_train, y_train)
    score = clf.score(X_test, y_test)
    DecisionBoundaryDisplay.from_estimator(
        clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5
    )

    ax.scatter(
        X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k"
    )
    ax.scatter(
        X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k"
    )

    ax.set_xlim(x_min, x_max)
    ax.set_ylim(y_min, y_max)
    ax.set_xticks(())
    ax.set_yticks(())
    if ds_cnt == 0:
        ax.set_title(name)
    ax.text(
        x_max - 0.3,
        y_min + 0.3,
        ("%.2f" % score).lstrip("0"),
        size=15,
        horizontalalignment="right",
    )
    i += 1

Summary

In this lab, we compared several classifiers in Scikit-Learn on synthetic datasets. We preprocessed the datasets, split them into training and test parts, and plotted the datasets. We then iterated over classifiers, fit the classifiers on the training data, plotted the decision boundaries, and plotted the testing data. Finally, we displayed the classification accuracy on the test set.