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,
svc = SVC(kernel='rbf'), train_y)
pred_y = svc.predict(test_x)
f1 = metrics.f1_score(test_y, pred_y)
print("The F1 score is {}.".format(f1))

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