F-beta-score

De F-beta-score is een gewogen harmonisch gemiddelde van precisie en recall, en wordt gebruikt om precisie en recall anders te wegen. Vaak wil je precisie zwaarder laten meewegen dan recall; dat kan met een lagere beta tussen 0 en 1. In deze oefening bereken je de precisie en recall van een MLP-classifier, samen met de F-beta-score met beta = 0.5.

X_train, y_train, X_test, y_test zijn beschikbaar in je werkruimte, en de features zijn al gestandaardiseerd. pandas als pd en sklearn zijn ook beschikbaar in je werkruimte. fbeta_score() uit sklearn.metrics is eveneens beschikbaar.

Deze oefening maakt deel uit van de cursus

CTR voorspellen met Machine Learning in Python

Cursus bekijken

Oefeninstructies

Splits de data in trainings- en testgegevens.
Definieer een MLP-classifier, train met .fit() en voorspel met .predict().
Gebruik implementaties uit sklearn om de precisie-, recall- en F-beta-scores te berekenen.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Set up MLP classifier, train and predict
X_train, X_test, y_train, y_test = ____(
  ____, ____, test_size = .2, random_state = 0)
clf = ____(hidden_layer_sizes = (16, ), 
                    max_iter = 10, random_state = 0)
y_pred = clf.____(____, _____).____(X_test) 

# Evaluate precision and recall
prec = ____(y_test, ____, average = 'weighted')
recall = ____(y_test, ____, average = 'weighted')
fbeta = ____(y_test, ____, ____  = 0.5, average = 'weighted')
print("Precision: %s, Recall: %s, F-beta score: %s" %(prec, recall, fbeta))

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

CTR voorspellen met Machine Learning in Python

SkillTag.level.intermediateSkillTag.label

4.9+

Begin de cursus gratis

Chances are you’re on this page because you clicked a link. In this chapter, you’ll learn why click-through-rates (CTR) are integral to targeted advertising, how to perform basic DataFrame manipulation, and how you can use machine learning models to predict CTR.

Exercise 1: Introduction to click-through rates Exercise 2: Beginning steps Exercise 3: Feature exploration Exercise 4: First evaluation of data Exercise 5: Overview of machine learning models Exercise 6: Logistic regression for breast cancer Exercise 7: Logistic regression for images Exercise 8: A second toy model Exercise 9: CTR prediction using decision trees Exercise 10: Model implementation Exercise 11: A first CTR model Exercise 12: Beyond only accuracy

This chapter provides the foundations for exploratory data analysis (EDA). Using sample data you’ll use the pandas library to look at columns and data types, explore missing data, and use hashing to perform feature engineering on categorical features. All of which are important when exploring features for more accurate CTR prediction.

Exercise 1: Exploratory data analysis Exercise 2: A first look Exercise 3: Checking for missing values Exercise 4: Distributions by CTR Exercise 5: Feature engineering Exercise 6: Analyzing datetime columns Exercise 7: Converting categorical variables Exercise 8: Creating new features Exercise 9: Standardizing features Exercise 10: Log normalization Exercise 11: Understanding standardization Exercise 12: Standard scaling

It’s time to dive deeper. Find out how you can use measures of model performance including precision and recall to answer real-world questions, such as evaluating ROI on ad spend. You’ll also learn ways to improve upon those evaluation metrics, such as ensemble methods and hyperparameter tuning.

Exercise 1: Applications of metric evaluation Exercise 2: Four categories of outcomes Exercise 3: Evaluating four categories Exercise 4: ROI on ad spend Exercise 5: Model evaluation Exercise 6: Precision and recall Exercise 7: Baseline Exercise 8: Classifier comparison Exercise 9: Tuning models Exercise 10: Regularization Exercise 11: Cross validation Exercise 12: Model selection Exercise 13: Ensembles and hyperparameter tuning Exercise 14: Understanding hyperparameter tuning Exercise 15: Random forests Exercise 16: Grid search

Profits can be heavily impacted by your campaign’s CTR. In this chapter, you’ll learn how deep learning can be used to reduce that risk. You’ll focus on multi-layer perceptron (MLP) and neural network models, and learn how these can be used to capture the complex relationship between variables to more accurately predict CTR. Lastly, you’ll explore how to apply the basics of hyperparameter tuning and regularization to classification models.

Exercise 1: Introductie tot deep learning Exercise 2: MLP's begrijpen Exercise 3: Startmodel Exercise 4: MLP's voor CTR Exercise 5: Hyperparametertuning in deep learning Exercise 6: Hyperparametertuning in MLP's Exercise 7: Variëren van hyperparameters Exercise 8: MLP Grid Search Exercise 9: Modelbeoordeling Exercise 10: F-beta-score

Huidige oefening

Exercise 11: Lage precision en hoge AUC Exercise 12: Precision, ROI en AUC Exercise 13: Modelbeoordeling en -vergelijking Exercise 14: Voorbereiding modelvergelijking Exercise 15: Precisie en ROI evalueren Exercise 16: Totale score Exercise 17: Afsluitende video