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. 

Pengantar validasi model

Langkah-langkah pemodelan

Data yang pernah vs. belum pernah dilihat

Model regresi

Atur parameter dan latih model

Tingkat kepentingan fitur

Model klasifikasi

Prediksi klasifikasi

Menggunakan kembali parameter model

Klasifikator random forest

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. 

Creating train, test, and validation datasets

Create one holdout set

Create two holdout sets

Why use holdout sets

Accuracy metrics: regression models

Mean absolute error

Mean squared error

Performance on data subsets

Classification metrics

Confusion matrices

Confusion matrices, again

Precision vs. recall

The bias-variance tradeoff

Error due to under/over-fitting

Am I 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

Langkah-langkah pemodelan

Validasi Model di Python

Latihan interaktif praktis