Visualizing classification model performance

In this exercise, you will be solving a classification problem where the "popularity" column in the music_df dataset has been converted to binary values, with 1 representing popularity more than or equal to the median for the "popularity" column, and 0 indicating popularity below the median.

Your task is to build and visualize the results of three different models to classify whether a song is popular or not.

The data has been split, scaled, and preloaded for you as X_train_scaled, X_test_scaled, y_train, and y_test. Additionally, KNeighborsClassifier, DecisionTreeClassifier, and LogisticRegression have been imported.

Deze oefening maakt deel uit van de cursus

Supervised Learning with scikit-learn

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Oefeninstructies

Create a dictionary of "Logistic Regression", "KNN", and "Decision Tree Classifier", setting the dictionary's values to a call of each model.
Loop through the values in models.
Instantiate a KFold object to perform 6 splits, setting shuffle to True and random_state to 12.
Perform cross-validation using the model, the scaled training features, the target training set, and setting cv equal to kf.

Praktische interactieve oefening

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# Create models dictionary
models = {"____": ____(), "____": ____(), "____": ____()}
results = []

# Loop through the models' values
for model in ____.____():
  
  # Instantiate a KFold object
  kf = ____(n_splits=____, random_state=____, shuffle=____)
  
  # Perform cross-validation
  cv_results = ____(____, ____, ____, cv=____)
  results.append(cv_results)
plt.boxplot(results, labels=models.keys())
plt.show()

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Deze oefening maakt deel uit van de cursus

Supervised Learning with scikit-learn

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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: How good is your model?Exercise 2: Deciding on a primary metric Exercise 3: Assessing a diabetes prediction classifier Exercise 4: Logistic regression and the ROC curve Exercise 5: Building a logistic regression model Exercise 6: The ROC curve Exercise 7: ROC AUC Exercise 8: Hyperparameter tuning Exercise 9: Hyperparameter tuning with GridSearchCV Exercise 10: Hyperparameter tuning with 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

Huidige oefening

Exercise 15: Pipeline for predicting song popularity Exercise 16: Congratulations