Pemusatan dan penskalaan untuk klasifikasi

Sekarang Anda akan menggabungkan penskalaan dan pembuatan model ke dalam sebuah pipeline untuk cross-validation.

Tugas Anda adalah membangun pipeline untuk men-skala fitur dalam himpunan data music_df dan melakukan grid search cross-validation menggunakan model logistic regression dengan berbagai nilai untuk hyperparameter C. Variabel target di sini adalah "genre", yang berisi nilai biner untuk rock sebagai 1 dan genre lainnya sebagai 0.

StandardScaler, LogisticRegression, dan GridSearchCV telah diimpor untuk Anda.

Latihan ini adalah bagian dari kursus

Supervised Learning dengan scikit-learn

Petunjuk latihan

Bangun langkah-langkah untuk pipeline: sebuah objek StandardScaler() bernama "scaler", dan model logistic regression bernama "logreg".
Buat parameters, dengan mencari 20 nilai float berjarak sama dari 0.001 hingga 1.0 untuk hyperparameter C model logistic regression di dalam pipeline.
Buat instance objek grid search.
Fit objek grid search ke data latih.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Build the steps
steps = [("____", ____()),
         ("____", ____())]
pipeline = Pipeline(steps)

# Create the parameter space
parameters = {"____": np.____(____, ____, 20)}
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 
                                                    random_state=21)

# Instantiate the grid search object
cv = ____(____, param_grid=____)

# Fit to the training data
cv.____(____, ____)
print(cv.best_score_, "\n", cv.best_params_)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Supervised Learning dengan scikit-learn

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Exercise 1: Machine learning with scikit-learn Exercise 2: Binary classification Exercise 3: The supervised learning workflow Exercise 4: The classification challenge Exercise 5: k-Nearest Neighbors: Fit Exercise 6: k-Nearest Neighbors: Predict Exercise 7: Measuring model performance Exercise 8: Train/test split + computing accuracy Exercise 9: Overfitting and underfitting Exercise 10: Visualizing model complexity

In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.

Exercise 1: Introduction to regression Exercise 2: Creating features Exercise 3: Building a linear regression model Exercise 4: Visualizing a linear regression model Exercise 5: The basics of linear regression Exercise 6: Fit and predict for regression Exercise 7: Regression performance Exercise 8: Cross-validation Exercise 9: Cross-validation for R-squared Exercise 10: Analyzing cross-validation metrics Exercise 11: Regularized regression Exercise 12: Regularized regression: Ridge Exercise 13: Lasso regression for feature importance

Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.

Exercise 1: How good is your model?Exercise 2: Deciding on a primary metric Exercise 3: Assessing a diabetes prediction classifier Exercise 4: Logistic regression and the ROC curve Exercise 5: Building a logistic regression model Exercise 6: The ROC curve Exercise 7: ROC AUC Exercise 8: Hyperparameter tuning Exercise 9: Hyperparameter tuning with GridSearchCV Exercise 10: Hyperparameter tuning with RandomizedSearchCV

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!

Exercise 1: Praproses data Exercise 2: Membuat variabel dummy Exercise 3: Regresi dengan fitur kategorikal Exercise 4: Menangani data hilang Exercise 5: Menghapus data hilang Exercise 6: Pipeline untuk prediksi genre lagu: I Exercise 7: Pipeline untuk prediksi genre lagu: II Exercise 8: Pemusatan dan penskalaan Exercise 9: Pemusatan dan penskalaan untuk regresi Exercise 10: Pemusatan dan penskalaan untuk klasifikasi

Latihan Saat Ini

Exercise 11: Mengevaluasi beberapa model Exercise 12: Memvisualisasikan kinerja model regresi Exercise 13: Memprediksi pada himpunan uji Exercise 14: Memvisualisasikan kinerja model klasifikasi Exercise 15: Pipeline untuk memprediksi popularitas lagu Exercise 16: Selamat