Menilai pengklasifikasi prediksi diabetes

Pada bab ini Anda akan bekerja dengan himpunan data diabetes_df yang telah diperkenalkan sebelumnya.

Tujuannya adalah memprediksi apakah setiap individu kemungkinan memiliki diabetes atau tidak berdasarkan fitur body mass index (BMI) dan usia (dalam tahun). Oleh karena itu, ini adalah masalah klasifikasi biner. Nilai target 0 menunjukkan bahwa individu tersebut tidak memiliki diabetes, sedangkan nilai 1 menunjukkan bahwa individu tersebut memiliki diabetes.

diabetes_df telah dimuat sebelumnya untuk Anda sebagai pandas DataFrame dan dibagi menjadi X_train, X_test, y_train, dan y_test. Selain itu, sebuah KNeighborsClassifier() telah diinisialisasi dan ditetapkan ke knn.

Anda akan melatih model, membuat prediksi pada himpunan uji, kemudian menghasilkan confusion matrix dan classification report.

Latihan ini adalah bagian dari kursus

Supervised Learning dengan scikit-learn

Petunjuk latihan

Impor confusion_matrix dan classification_report.
Latih model pada data pelatihan.
Prediksi label himpunan uji, lalu simpan hasilnya sebagai y_pred.
Hitung dan cetak confusion matrix dan classification report untuk label uji dibandingkan label prediksi.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import confusion matrix
____

knn = KNeighborsClassifier(n_neighbors=6)

# Fit the model to the training data
____

# Predict the labels of the test data: y_pred
y_pred = ____

# Generate the confusion matrix and classification report
print(____(____, ____))
print(____(____, ____))

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: Seberapa baik model Anda?Exercise 2: Menentukan metrik utama Exercise 3: Menilai pengklasifikasi prediksi diabetes

Latihan Saat Ini

Exercise 4: Regresi logistik dan kurva ROC Exercise 5: Membangun model logistic regression Exercise 6: Kurva ROC Exercise 7: ROC AUC Exercise 8: Penyetelan hiperparameter Exercise 9: Penyetelan hyperparameter dengan GridSearchCV Exercise 10: Hyperparameter tuning dengan 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: Preprocessing data Exercise 2: Creating dummy variables Exercise 3: Regression with categorical features Exercise 4: Handling missing data Exercise 5: Dropping missing data Exercise 6: Pipeline for song genre prediction: I Exercise 7: Pipeline for song genre prediction: II Exercise 8: Centering and scaling Exercise 9: Centering and scaling for regression Exercise 10: Centering and scaling for classification Exercise 11: Evaluating multiple models Exercise 12: Visualizing regression model performance Exercise 13: Predicting on the test set Exercise 14: Visualizing classification model performance Exercise 15: Pipeline for predicting song popularity Exercise 16: Congratulations