# 10 ML Algorithms Flashcards

1
Q

Example of Ensemble ML Methods?

A
• Bayesian averaging
• bagging
• boosting
• error-correcting output coding
2
Q

What are the main clustering methodologies?

A
• Centroid-based algorithms
• Connectivity-based algorithms
• Density-based algorithms
• Probabilistic
• Dimensionality Reduction
• Neural networks / Deep Learning
3
Q

What is Singular Value Decomposition?

A

In linear algebra, SVD is a factorization of a real complex matrix. For a given m * n matrix M, there exists a decomposition such that M = UΣV, where U and V are unitary matrices and Σ is a diagonal matrix.
PCA is actually a simple application of SVD.

4
Q

Top supervised ML algorithms:

A

1) Support Vector Machines
2) Ensemble Methods
3) Logistic Regression
4) Ordinary Least Squares Regression
5) Naïve Bayes Classification
6) Decision Trees.

5
Q

Machine learning algorithms can be divided into 3 broad categories —  1, 2 ,3

A

supervised learning, unsupervised learning, and reinforcement learning.

6
Q

What is Reinforcement learning?

A

Between supervised and unsupervised. There is some form of feedback available for each predictive step or action, but no precise label or error message.

7
Q

What are Ensemble ML Methods?

A

Learning algorithms that construct a set of classifiers and then classify new data points by taking a weighted vote of their predictions

8
Q

Applications of Independent Component Analysis (ICA):

A
• digital images,
• document databases,
• economic indicators
• psychometric measurements.
9
Q

What is Principal Component Analysis?

A

PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

10
Q

Naïve Bayes Classification examples:

A
• email as spam or not spam
• news article about technology, politics, or sports
• Used for face recognition software
11
Q

What is clustering?

A

Clustering is the task of grouping a set of objects such that objects in the same group (cluster) are more similar to each other than to those in other groups.

12
Q

What is an Independent Component Analysis (ICA)?

A
• ICA is a statistical technique for revealing hidden factors that underlie sets of random variables, measurements, or signals.
• ICA is related to PCA, but it is a much more powerful technique that is capable of finding the underlying factors of sources when these classic methods fail completely.
13
Q

What is the advantage of SVM?

A
• biggest problems that have been solved using SVMs
• large-scale image classification
• Used when the number of features is big
14
Q

how do ensemble methods work and why are they superior to individual models?

A
• They average out biases
• They reduce the variance
• They are unlikely to over-fit
15
Q

What is the advantage of Decision Trees?

A

As a method, it allows you to approach the problem in a structured and systematic way to arrive at a logical conclusion.

16
Q

Naïve Bayes Classification Components:

A
• P(A|B) is posterior probability,
• P(B|A) is likelihood,
• P(A) is class prior probability,
• P(B) is predictor prior probability.
17
Q

What is Logistic Regression?

A

It measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative logistic distribution.

18
Q

Top unsupervised algorithms:

A

1) Clustering Algorithms
2) Principal Component Analysis
3) Singular Value Decomposition
4) Independent Component Analysis

19
Q

What is Ordinary Least Squares Regression (OLSR)?

A
• Least squares is a method for performing linear regression.
• Linear regression as the task of fitting a straight line through a set of points.
• Linear refers the kind of model you are using to fit the data, while least squares refers to the kind of error metric you are minimizing over
20
Q

What we do PCA?

A
• compression,
• simplifying data for easier learning,
• visualization
• It is not suitable in cases where data is noisy
21
Q

Logistic Regression applications

A
• Credit Scoring
• Measuring the success rates of marketing campaigns
• Predicting the revenues of a certain product