Rule-Based Machine Learning Quiz: Test Your Knowledge

Minimize the error function

Find the best hyperparameters

Discover hidden patterns and relationships in data

Identify rules and make decisions based on them

As decision trees

As a sequence of numbers

As a set of if-then statements

As a probability distribution

The action to be taken

The condition to check

The decision boundary

The number of iterations

Support Vector Machine

Decision Tree Learning

Random Forest

Apriori Algorithm

Make predictions

Split the database

Store hyperparamenters

Computer gradients

To store the data samples

To represent the output variables

To describe the conditions or criteria for the rule

To perform feature scaling

The model is too simple to capture the underlying patterns.

The model performs well on the training data but poorly on unseen data.

The model is biased and underestimates the target variable.

The model has too many features and is computationally expensive.

Reinforcement Learning

Supervised Learning

Unsupervised Learning

Rule-Based Learning

To handle exceptions to the rules

To apply feature scaling

To define the output or action to be taken if the conditions are met

To validate the rule against the test data

A feature/column

The target variable

A decision rule

A leaf node

To find frequent itemsets

To classify data

To perform regression

To cluster data

K-Nearest Neighbors

Linear Regression

Decision Tree

Random Forest

Interpretable

Easily scalable

Fewer data requirements

Can handle missing values

Objects with similar attributes are in proximity in space

Objects with different attributes are in proximity in space

Objects with similar attributes are far apart in space

Objects with different attributes are far apart in space

Euclidean Distance

Pearson Correlation

Cosine Similarity

Jaccard Similarity