Machine Learning

Question 1

Hierarchical clustering is most likely used when the problem involves

Answer 1

Classifying unlebeled data
and when number of categories are unknown

Question 2

What is Supervised machine learning

Answer 2

Involves training an algorithm to take a set of inputs (x variables) and find a model that best relates them to outputs (Y variables)

Training algorithm - Set of inputs - find models that relates to outputs.

Question 3

What is unsupervised machine learning

Answer 3

Same as supervised learning, but does not make use of labeled training data.

We give it data and expect the algorithm to make sense of it.

Question 4

What is Overfitting

Answer 4

ML models can produce overly complex models that may fit the training data too well and thereby not generalize new data well.

The prediction model of the traning sample (in-Sample data) is too complex.

The traning Data does not work well with the new data

Question 5

Name Supervised ML Algorithms

Answer 5

Penalized regression
Support Vector Machine (SVM)
K - Nearest Neighbor
Classification and Regression Trees (CART)
Ensemble learning
Random Forest

Question 6

Name unsupervised ML Algortihms

Answer 6

Principle component analysis
K-Mean clustering
Hierarachical clustering

Question 7

High Bias Error in ML

Answer 7

High Bias Error means the model does not fit the training data well.

Question 8

High Variance Error in ML

Answer 8

High variance error means the model does not predict well on the test data

Question 9

Name Dimension Reduction in ML

Answer 9

Principle component analysis (unsupervised ML)

Penalized Regression
(Supervised ML)

Question 10

What does Penalized Regression do?

Answer 10

• Simmilar to maximizing adjusted R square.
• Demension Reduction
• Eliminates/minimazie overfitting

Regression coefficients are chosen to minimize the sum of the squared error, plus a penalty term that increases with the number of included features

Question 11

What is SVM

Answer 11

Support Vector Machine

It is Classification, Regression, and Outlier detection
Classifying data that is not complex or non-linear.

Is a linear classifier that determines the hyperplane that optimally seperates the observation into two sets of data points.

Does not requier any hyperparameter.

Maximize the probability of making a correct prediction by determining the boundry that is furthest from all observation.

Outliers do not affect either the support vectors or the discriminant boundry.

Question 12

What is K-Nearest Neighbor

Answer 12

Classification

Classify new observation by finding similarities in the existing data.

Makes no assumption about the distribution of the data.
It is non-parametric.

KNN results can be sensitive to inclusion of irrelevant or correlated featuers, so it may be neccessary to select featuers manually.
Thereby removing less irrelevant information.

Question 13

What is CART

Answer 13

Classification and Regression Trees

Part of supervised ML

Typically applied when the target is binary.

If the goal is regression, the prediction would be the mean of the values of the terminal node.

Makes no assumption about the characteristics of the traning data, so if left unconstrained, potentially it can perfectly learn the traning data.

To avoid overfitting, regulation paramterers can be added, such as the maximum dept of the tree.

Question 14

What are the 3 types of layer in Neural Network

Answer 14

1. Input layer
2. Hidden layer
3. Output layer

Question 15

What are non-linear functions more susceptiable to?

Answer 15

Variance error and overfitting

Question 16

What are linear functions more susceptiable to?

Answer 16

Bias error and underfitting

Question 17

The main distinction between clustering and classification algorithms is that

Answer 17

The groups in clustering are determined by the data

Classification they are determined by the analyst/researcher

Question 18

What is K-Means clustering in ML?

Answer 18

K-means partitions observations into a fixed number, k, of non-overlaping cluster.

Each cluster is characterized by its centroid, and each observation is assigned by the algorithm to the cluster with the centroid to which that observation is closest.

Question 19

High bias error and high variance error are indicative of…

Answer 19

Underfitting

High bias error = model does not fit on the traning data.

High variance = Model does not predict well on test data.

Both combination results in a underfitted model.

Question 20

Low bias error but high variance error is indicative of ..

Answer 20

Overfitting

Bias error = model does not fit the traning data well.

Variance error = Model does not predict well on test data.

Question 21

What are linear models more susceptible to?

Answer 21

Bias Error (underfitting)

Question 22

What are non-linear models more prone to?

Answer 22

Variance Error
(overfitting)

Question 23

What is Principal Components Analysis

Answer 23

It is part of unsupervised ML
Dimension Reduction

Use to reduce highly correlaed featuers of data into few main uncorrelated composite variables.

Question 24

What are the 3 types of error in ML?

Answer 24

Bias error
Variance error
Base error

Question 25

What is variance error in ML?

Answer 25

Variance Error or how much the model’s results change in response to new data from validation and test samples. Unstable models pick up noise and produce high variance causing overfitting and ↑ out of-sample error.

Question 26

What is Bias error in ML?

Answer 26

Bias Error or the degree to which a model fits the training data. Algorithms with erroneous assumptions produce high bias with poor approximation, causing underfitting and ↑ in-sample error. (Adding more training samples will not improve the model)

Question 27

What is Bias error in ML?

Answer 27

Base Error due to randomness in the data. (Out-of-sample accuracy increases as the training sample size increases)

Question 28

Name 2 ways to Preventing Overfitting in Supervised Machine Learning

Answer 28

**Ocean’s Razor**: The problem solving principle that the simplest solution tends to be the correct one. In supervised ML, it means preventing the algorithm from getting too complex during selection and training by limiting the no. of features and penalizing algorithms that are too complex or too flexible by constraining them to include only parameters that reduce out-of-sample error. **K-Fold Cross Validation**: This strategy comes from the principle of avoiding sampling bias. The challenge is having a large enough data set to make both training and testing possible on representative samples.