Fundamentals of Machine Learning I (Not Part of Certification)

Question 1

Characteristics of Regression?

Answer 1

• Predicts amount of something based on numerical values
• Uses data splitting and penalty to get an effective mathematical function
• Supervised
• Metrics
  • MAE, MSE, RMSE, R^2

Question 2

What is binary classification + characteristics

Answer 2

• Prediction of one of two classes ex(diabetic to not diabetic)
• Supervised
• Uses probability
• Classifies probability with sigmoid curve
• Logistic Regression is a binary classifier
• made with random subset of data
• Confusion Matrixes
• F1, Recall, Precision, AUC, TPR, FPR, ROC

Question 3

What is RMSE

Answer 3

RMSE (Root Mean Squared Error) Used to measure number of
incorrect predictions

Question 4

What is MAE

Answer 4

MAE (Mean Absolute Error) Calculated with mean error

Question 5

What is F1

Answer 5

F1 Score

• Harmonic mean of Precision and Recall.
• Formula: 2 * (Precision * Recall) / (Precision + Recall).

Question 6

What is Recall

Answer 6

Recall (Sensitivity, TPR)

• Proportion of actual positives correctly identified by the model.
• Formula: TP / (TP + FN).

Question 7

What is AUC

Answer 7

AUC (Area Under the Curve)

• Measures overall classification performance as the area under the ROC curve.
• Values range from 0 to 1, with 1 being perfect and 0.5 representing random guessing.

Question 8

What is TPR

Answer 8

TPR (True Positive Rate, Recall)

• Proportion of actual positives classified as positive.
• Formula: TP / (TP + FN).

Question 9

What is Precision

Answer 9

Precision

• Proportion of predicted positives that are actual positives.
• Formula: TP / (TP + FP).

Question 10

What is FPR

Answer 10

FPR (False Positive Rate)

• Proportion of actual negatives incorrectly classified as positive.
• Formula: FP / (FP + TN).

Question 11

What is ROC

Answer 11

ROC (Receiver Operating Characteristic) Curve

• Plots TPR vs. FPR across different thresholds.
• Used to assess model performance over varying decision boundaries.

Question 12

What is R^2

Answer 12

R^2 (Coefficient of determination) Used to measure variance in
data to calculate the fit of the model

Question 13

What is MSE

Answer 13

MSE (Mean squared error) Mean of error amount squared. Used
to amplify the error amount

Question 14

What is multiclass classification?

Answer 14

Multiclass classification is used to predict which of multiple possible classes an observation belongs to. It calculates probability values for each class label and predicts the most probable class.

(Supervised)

Question 15

What are the two types of algorithms used in multiclass classification?

Answer 15

The two types of algorithms are:

One-vs-Rest (OvR): Trains a binary classification function for each class.
Multinomial: Creates a single function that returns a probability distribution for all possible classes.

Question 16

How does the One-vs-Rest (OvR) algorithm work?

Answer 16

The OvR algorithm trains separate binary classification functions for each class. Each function calculates the probability that an observation belongs to a specific class compared to all others, and the class with the highest probability is predicted.

Question 17

What is a multinomial algorithm, and how does it work?

Answer 17

A multinomial algorithm creates a single function that returns a vector with probability values for each class. These values add up to 1, and the class with the highest probability is predicted. An example is the softmax function.

Question 18

How can you evaluate a multiclass classification model?

Answer 18

You can evaluate by calculating binary classification metrics for each class or aggregate metrics across all classes. Metrics like accuracy, recall, precision, and F1-score can be derived from a multiclass confusion matrix.

Question 19

How are binary classification metrics used in evaluating multiclass classification models?

Answer 19

In multiclass classification, binary metrics such as accuracy, recall, precision, and F1-score can be calculated for each individual class. These are derived from the confusion matrix, where True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN) are recorded for each class. Aggregate metrics can then be calculated for overall model evaluation.

Question 20

What metrics can be derived from a multiclass confusion matrix?

Answer 20

Metrics derived from a multiclass confusion matrix include:

Accuracy, Recall, Precision, F1-Score

Question 21

Define Unsupervised Learning

Answer 21

Unsupervised learning uses data without labels to find patterns or groupings. Examples: clustering, dimensionality reduction.

Question 22

Define Supervised Learning

Answer 22

Supervised learning uses labeled data (features and known outcomes) to train a model to make predictions. Examples: classification, regression.

Question 23

What is clustering in machine learning?

Answer 23

Clustering is an unsupervised learning method where observations are grouped into clusters based on similarities in their features, without using labels.

Question 24

Why is clustering considered unsupervised learning?

Answer 24

Clustering is unsupervised because it doesn’t rely on known label values. Instead, it groups data points based solely on feature similarities.

Question 25

What is an example of clustering?

Answer 25

In a flower dataset with features like the number of leaves and petals, clustering groups similar flowers based on these features without knowing their species.

Question 26

What is the centroid process in K-Means clustering?

Answer 26

1. Initialization: Choose 𝑘 clusters and randomly select 𝑘 initial centroids.
2. Assignment Step: Calculate distances from each point to centroids, assigning points to the nearest centroid.
3. Update Step: Recalculate centroids by finding the mean of assigned points.
4. Repeat: Iterate the assignment and update steps until centroids stabilize or a maximum number of iterations is reached.

Question 27

What is K-Means clustering?

Answer 27

K-Means is a clustering algorithm that assigns data points to clusters by minimizing the distance to the cluster’s centroid, repeatedly adjusting centroids until stable clusters are formed.

Question 28

How do you evaluate a clustering model?

Answer 28

Clustering models are evaluated by how well the clusters are separated, using metrics like average distance to centroid, maximum distance, and silhouette score

Question 29

What is a silhouette score in clustering?

Answer 29

A silhouette score measures cluster separation, ranging from -1 to 1, where values closer to 1 indicate better-defined clusters.

Question 30

What metrics are used to evaluate clusters?

Answer 30

Metrics include average distance to cluster center, average distance to other centers, maximum distance to center, and silhouette score.

Question 31

What is a Centroid

Answer 31

A centroid is the central point of a cluster, representing the mean position of all data points assigned to that cluster in the feature space. It serves as the reference for assigning new points to clusters in algorithms like K-Means.