Advanced Flashcards

(18 cards)

1
Q

What is machine learning in the context of building mathematical models?

A

Machine learning automates the process of building mathematical models out of data.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

What is linear regression?

A

Linear regression is a linear model that fits the best fit line through data to establish the relationship between independent and dependent variables.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

What is the equation of the best fit line in linear regression?

A

The equation can be given as: Y = ax1 + bx2 + c, where a and b are coefficients, and c is the constant.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

How does linear regression minimize errors?

A

Linear regression tries to fit the line so that the predicted values are closer to the observed values.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

How does linear regression automate model building?

A

It automatically finds the best fit line that has the minimum error.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

What is the train-validation split method?

A

In this method, the training set is divided into two parts: one for training and the other for validating model performance.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

What is K-fold cross-validation?

A

K-fold cross-validation divides the training set into k-folds, using each fold for testing while the others are used for training.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

What is correlation?

A

Correlation is a statistical measure that expresses the strength of a linear relationship between two quantitative variables.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

What is multicollinearity?

A

Multicollinearity occurs when two or more variables have a strong linear relationship, making it hard for models to distinguish their individual effects.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

How can multicollinearity be detected?

A

It can be detected using the Variance Inflation Factor (VIF).

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

How would you explain a Decision Tree to a non-tech person?

A

A decision tree is an inverted tree representation that mimics human decision-making by representing possible solutions based on conditions.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

What is pre-pruning in decision trees?

A

Pre-pruning restricts the decision tree before it grows fully by bounding its depth.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

What is post-pruning in decision trees?

A

Post-pruning allows the tree to grow fully and then prunes sub-trees that do not provide significant information.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

How does a Random Forest model differ from using ‘n’ decision trees?

A

A Random Forest uses bootstrapping in rows and columns, creating different datasets for each tree, while ‘n’ decision trees use the same training data.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

What is the elbow method in K-Means clustering?

A

The elbow method involves iterating over a range of K values and calculating the within-cluster sum of squares (WCSS) to identify the optimal number of clusters.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Why is understanding the bias-variance trade-off important?

A

It helps balance model complexity to avoid underfitting (high bias) and overfitting (high variance) for better performance.

17
Q

Is the bias-variance trade-off applicable to Deep Neural Networks?

A

Yes, but it may not strictly apply as neural networks can handle increased complexity and data better than traditional algorithms.

18
Q

How do neural networks compare to other non-linear ML algorithms in decision boundaries?

A

Neural networks create the most complex decision boundaries due to their hierarchical nature, while Decision Trees create piecewise linear splits.