Hands-on Machine Learning with Scikit-Learn/

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Challenge Solution Review

Challenge Solution Review

In this lesson, we explain the solution to the last challenge lesson.

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import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
import sklearn.metrics as metrics
df = pd.read_csv("./nonlinear.csv", sep=",", header=0)
y = df.pop("target").values
X = df
train_x, test_x, train_y, test_y = train_test_split(X,
                                                    y,
                                                    test_size=0.2,
                                                    random_state=42)
svc = SVC(kernel='rbf')
svc.fit(train_x, train_y)
pred_y = svc.predict(test_x)
f1 = metrics.f1_score(test_y, pred_y)
print("The F1 score is {}.".format(f1))

First, you need to load the dataset from nonlinear.csv by read_csv at line 6. Here we use the pandas library, which is a widely used library for data processing. If you are not familiar with this library, you also can check another course, ...