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. 

Train-, test- en validatiegegevenssets maken

Maak één holdoutset

Maak twee holdout-sets

Waarom holdout-sets gebruiken

Nauwkeurigheidsmaten: regressiemodellen

Gemiddelde absolute fout

Mean squared error

Prestaties op gegevenssubsets

Classificatiemetrics

Confusion matrices

Nogmaals confusion matrices

Precision vs. recall

De bias-variance trade-off

Fout door under/overfitting

Heb ik last van 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

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

Waarom holdout-sets gebruiken

Modelvalidatie in Python

Praktische interactieve oefening