Summary - scikit-learn
- Machine Learning course
- Intermediate
- Lesson 6/112
- 6 min
In this group you learned the scikit-learn estimator API.
Summary: scikit-learn
In this group:
- The shared interface:
fit,predict,transform,score; models swap with one line. - Estimator roles: predictor, transformer; hyperparameters in the constructor, learned values with
_. clone,get_params,set_params.predict_proba,decision_function;reshape(1, -1)for a single example.- The default
score; custom scorers withget_scorerandmake_scorer.
Practice Comparison on synthetic data
Generate data with make_moons(n_samples=300, noise=0.25, random_state=0) and split it with test_size=0.3, random_state=0. Train LogisticRegression() and SVC(); on each line print the class name and the test accuracy with round(..., 3).
Starter code
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
# buraya yazSample solution
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
X, y = make_moons(n_samples=300, noise=0.25, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
for m in [LogisticRegression(), SVC()]:
m.fit(X_train, y_train)
print(type(m).__name__, round(m.score(X_test, y_test), 3))Expected output
LogisticRegression 0.844 SVC 0.967
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 Why does cross-validation use <code>clone</code> in every fold?
- To share the weights
- It doesn't
- So every fold starts with a clean, untrained model Correct answer
- Because it is fast
Explanation: What was learned in the previous fold must not affect the next fold's score.
2 Get the F1 scorer by name.
Question
olcut = ____("f1_macro")
Answer
olcut = get_scorer("f1_macro")Explanation: get_scorer returns the same scorer as the names in the scoring parameter.
3 Match the tools.
| Term | Meaning |
|---|---|
make_classification |
Synthetic data |
clone |
Clean copy |
decision_function |
Raw score |
make_scorer |
Custom metric |