Een logistiek regressiemodel bouwen

In deze oefening bouw je een logistiek regressiemodel met alle features in de gegevensset diabetes_df. Het model wordt gebruikt om de kans te voorspellen dat personen in de testset een diabetesdiagnose hebben.

De gegevensset diabetes_df is al voor je opgesplitst in X_train, X_test, y_train en y_test, en vooraf geladen.

Deze oefening maakt deel uit van de cursus

Supervised Learning met scikit-learn

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Oefeninstructies

Importeer LogisticRegression.
Maak een instantie van een logistiek regressiemodel, logreg.
Fit het model op de trainingsgegevens.
Voorspel de kansen dat elke persoon in de testset een diabetesdiagnose heeft, en sla de array met positieve kansen op als y_pred_probs.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import LogisticRegression
____

# Instantiate the model
logreg = ____

# Fit the model
____

# Predict probabilities
y_pred_probs = logreg.____(____)[____, ____]

print(y_pred_probs[:10])

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Supervised Learning met scikit-learn

SkillTag.level.intermediateSkillTag.label

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In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Exercise 1: Machine learning with scikit-learn Exercise 2: Binary classification Exercise 3: The supervised learning workflow Exercise 4: The classification challenge Exercise 5: k-Nearest Neighbors: Fit Exercise 6: k-Nearest Neighbors: Predict Exercise 7: Measuring model performance Exercise 8: Train/test split + computing accuracy Exercise 9: Overfitting and underfitting Exercise 10: Visualizing model complexity

In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.

Exercise 1: Introduction to regression Exercise 2: Creating features Exercise 3: Building a linear regression model Exercise 4: Visualizing a linear regression model Exercise 5: The basics of linear regression Exercise 6: Fit and predict for regression Exercise 7: Regression performance Exercise 8: Cross-validation Exercise 9: Cross-validation for R-squared Exercise 10: Analyzing cross-validation metrics Exercise 11: Regularized regression Exercise 12: Regularized regression: Ridge Exercise 13: Lasso regression for feature importance

Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.

Exercise 1: Hoe goed is je model?Exercise 2: Kiezen van een primaire metriek Exercise 3: Een classifier voor diabetesvoorspelling beoordelen Exercise 4: Logistische regressie en de ROC-curve Exercise 5: Een logistiek regressiemodel bouwen

Huidige oefening

Exercise 6: De ROC-curve Exercise 7: ROC AUC Exercise 8: Hyperparametertuning Exercise 9: Hyperparameter-tuning met GridSearchCV Exercise 10: Hyperparameter-tuning met RandomizedSearchCV

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!

Exercise 1: Preprocessing data Exercise 2: Creating dummy variables Exercise 3: Regression with categorical features Exercise 4: Handling missing data Exercise 5: Dropping missing data Exercise 6: Pipeline for song genre prediction: I Exercise 7: Pipeline for song genre prediction: II Exercise 8: Centering and scaling Exercise 9: Centering and scaling for regression Exercise 10: Centering and scaling for classification Exercise 11: Evaluating multiple models Exercise 12: Visualizing regression model performance Exercise 13: Predicting on the test set Exercise 14: Visualizing classification model performance Exercise 15: Pipeline for predicting song popularity Exercise 16: Congratulations