Before we can validate models, we need an understanding of how to create and work with them. This chapter provides an introduction to running regression and classification models in scikit-learn. We will use this model building foundation throughout the remaining chapters. 

Introduction to model validation

Modeling steps

Seen vs. unseen data

Regression models

Set parameters and fit a model

Feature importances

Classification models

Classification predictions

Reusing model parameters

Random forest classifier

Basic Modeling in scikit-learn

This chapter focuses on the basics of model validation. From splitting data into training, validation, and testing datasets, to creating an understanding of the bias-variance tradeoff, we build the foundation for the techniques of K-Fold and Leave-One-Out validation practiced in chapter three. 

Membuat himpunan data train, test, dan validation

Buat satu holdout set

Buat dua himpunan holdout

Mengapa menggunakan holdout set

Metrik akurasi: model regresi

Mean absolute error

Mean squared error

Kinerja pada subset data

Metrik klasifikasi

Confusion matrix

Matriks kebingungan, lagi

Precision vs. recall

Pertukaran bias-varians

Galat akibat under/overfitting

Apakah saya underfitting?

Validation Basics

Holdout sets are a great start to model validation. However, using a single train and test set if often not enough. Cross-validation is considered the gold standard when it comes to validating model performance and is almost always used when tuning model hyper-parameters. This chapter focuses on performing cross-validation to validate model performance. 

The problems with holdout sets

Two samples

Potential problems

Cross-validation

scikit-learn's KFold()

Using KFold indices

sklearn's cross_val_score()

scikit-learn's methods

Implement cross_val_score()

Leave-one-out-cross-validation (LOOCV)

When to use LOOCV

Leave-one-out-cross-validation

Cross Validation

The first three chapters focused on model validation techniques. In chapter 4 we apply these techniques, specifically cross-validation, while learning about hyperparameter tuning. After all, model validation makes tuning possible and helps us select the overall best model. 

Introduction to hyperparameter tuning

Creating Hyperparameters

Running a model using ranges

RandomizedSearchCV

Preparing for RandomizedSearch

Implementing RandomizedSearchCV

Selecting your final model

Best classification accuracy

Selecting the best precision model

Course completed!

Selecting the best model with Hyperparameter tuning. 

Candy dataset

Tic-Tac-Toe dataset

Model Machine Learning kini lebih mudah diimplementasikan daripada sebelumnya. Tanpa validasi yang tepat, hasil menjalankan data baru melalui sebuah model mungkin tidak seakurat yang diharapkan. Validasi model memungkinkan analis dengan yakin menjawab pertanyaan, seberapa baik model Anda? Kita akan menjawab pertanyaan ini untuk model klasifikasi menggunakan himpunan lengkap skenario akhir permainan tic-tac-toe, dan untuk model regresi menggunakan himpunan data peringkat kekuatan permen Halloween dari fivethirtyeight. Dalam kursus ini, kita akan membahas dasar-dasar validasi model, mendiskusikan berbagai teknik validasi, dan mulai mengembangkan alat untuk membuat model yang tervalidasi dan berkinerja tinggi.

Supervised Learning with scikit-learn

Pelajari dasar validasi model, teknik validasi, dan mulai buat model yang tervalidasi dan berkinerja tinggi.

Validasi Model di Python

Pelajari dasar-dasar validasi model, teknik validasi, dan mulailah membuat model yang tervalidasi dan berkinerja tinggi.

Ilmuwan Pembelajaran Mesin dalam Python

Mengapa menggunakan holdout set

Validasi Model di Python

Latihan interaktif praktis