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. 

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. 

Introductie tot hyperparameter tuning

Hyperparameters aanmaken

Een model draaien met bereiken

RandomizedSearchCV

Voorbereiden op RandomizedSearch

RandomizedSearchCV implementeren

Je definitieve model kiezen

Beste classificatienauwkeurigheid

Het beste precisie-model selecteren

Cursus voltooid!

Selecting the best model with Hyperparameter tuning. 

Candy dataset

Tic-Tac-Toe dataset

Machine Learning-modellen zijn tegenwoordig makkelijker te implementeren dan ooit. Zonder goede validatie zijn de resultaten van nieuwe data door een model halen mogelijk minder accuraat dan verwacht. Modelvalidatie stelt analisten in staat om vol vertrouwen de vraag te beantwoorden: hoe goed is je model? We beantwoorden deze vraag voor classificatiemodellen met de volledige set eindspel-scenario’s van boter-kaas-en-eieren, en voor regressiemodellen met fivethirtyeight’s ultimate Halloween candy power ranking-gegevensset. In deze cursus behandelen we de basis van modelvalidatie, bespreken we verschillende validatietechnieken en beginnen we tools te ontwikkelen om gevalideerde en goed presterende modellen te bouwen.

Supervised Learning with scikit-learn

Leer de basis van modelvalidatie, technieken en maak gevalideerde, goed presterende modellen.

Modelvalidatie in Python

Leer de basis van modelvalidatie en validatietechnieken, en begin met het maken van gevalideerde en goed presterende modellen.

Wetenschapper op het gebied van machine learning in Python

Beste classificatienauwkeurigheid

Modelvalidatie in Python

Praktische interactieve oefening