10 Statistical Techniques

Question 1

What is a Random forest?

Answer 1

• • very similar to bagging.
• • bootstrap samples of your training set.
• • faster, because each tree learns only from a subset of features.
• • in bagging, you give each tree the full set of features

Question 2

“data scientist is a person who is better at statistics than any ——– and better at programming than any ———– .”

Answer 2

programmer, statistician

Question 3

The two best-known techniques for shrinking the coefficient estimates towards zero

Answer 3

ridge regression and the lasso

Question 4

what is Bootstrapping in Resampling?

Answer 4

sampling with replacement from the original data, and take the “not chosen” data points as test cases. We can make this several times and calculate the average score as estimation of our model performance.

Question 5

techniques for Non linear models

Answer 5

• • step function
• • piecewise function
• • spline
• -generalized additive model

Question 6

What is Classification?

Answer 6

a data mining technique that assigns categories to a collection of data in order to aid in more accurate predictions and analysis.

Question 7

Do body weight calorie intake, fat intake, and participant age have an influence on heart attacks (Yes vs No)?

Answer 7

Logistic Regression

Question 8

Types of Dimensionality reduction?

Answer 8

1) Principal Components Regression (unsupervised)

2) Partial least squares (PLS) (supervised)

Question 9

How Discriminant Analysis works?

Answer 9

use Bayes’ theorem to provide propability that a new member belongs to which class.

Question 10

Three Tree-Based Methods

Answer 10

1) Bagging
2) Boosting
3) Random forest

Question 11

Why study Statistical Learning?

Answer 11

It is important to understand the ideas behind the various techniques, in order to know how and when to use them.

Question 12

What is the advantage of Boosting?

Answer 12

By combining the advantages and pitfalls by varying your weighting formula you can come up with a good predictive force for a wider range of input data, using different narrowly tuned models.

Question 13

Types of  Subset Selection?

Answer 13

1) Best-Subset Selection
2) Forward Stepwise Selection
3) Backward Stepwise Selection
4) Hybrid Methods

Question 14

What is Support Vector Machines (SVM)?

Answer 14

a hyperplane is n-1 dimensional subspace of an n-dimensional space) that best separates two classes of points with the maximum margin.

Question 15

what is Principal Component Analysis(PCA) ?

Answer 15

Producing low dimensional representation of the dataset by identifying a set of linear combination of features which have maximum variance and are mutually un-correlated.
* Understanding latent interaction between the variable in an unsupervised setting

Question 16

How to solve “How does the probability of getting lung cancer (Yes vs No) change for every additional pound of overweight and for every pack of cigarettes smoked per day?”

Answer 16

Logistic Regression

Question 17

What is Bagging

Answer 17

Way to decrease the variance of your prediction by generating additional data for training from your original dataset using combinations with repetitions to produce multistep of the same carnality/size as your original data

Question 18

What is Best-Subset Selection?

Answer 18

Choose the model with the highest R² and lowest RSS on testing error estimates.

Question 19

Types Discriminant Analysis

Answer 19

Linear and Quadratic.

Question 20

What technique to solve: How monthly income and trips per month are correlated with monthly spending?

Answer 20

Multiple Linear Regression

Question 21

What is Dimensionality reduction?

Answer 21

Dimension reduction reduces the problem of estimating p + 1 coefficients to the simple problem of M + 1 coefficients, where M < p.

Question 22

2 major Classification techniques

Answer 22

1) Logistic Regression

2) Discriminant Analysis.

Question 23

Types of Linear Regression.

Answer 23

1 – Simple Linear Regression

2–Multiple Linear Regression

Question 24

What is Unsupervised Learning?

Answer 24

1) Principal Component Analysis
2) k-Means clustering
3) Hierarchical clustering

Question 25

What is cross validation?

Answer 25

a technique for validating the model performance, We take the k — 1 parts as our training set and use the “held out” part as our test set. We repeat that k times differently. Finally, we take the average of the k scores as our performance estimation.

Question 26

What is Shrinkage (regularization)?

Answer 26

a model involving all p predictors, however, the estimated coefficients are shrunken towards zero relative to the least squares estimates.

Question 27

what is Subset Selection?

Answer 27

identifies a subset of the p predictors that we believe to be related to the response. We then fit a model using the least squares of the subset features.

Question 28

What are Nonlinear Models?

Answer 28

a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables.

Question 29

What is Boosting?

Answer 29

approach to calculate the output using several different models and then average the result using a weighted average approach.

Question 30

Differences between statistical learning and machine learning

Answer 30

• ML subfield of AI.
• Statistical learning subfield of Statistics.
• ML emphasis on large-scale applications and prediction accuracy.
• Statistical learning emphasizes models and their interpretability, and precision and uncertainty.

Question 31

10 Statistical Techniques

Answer 31

1 — Linear Regression
2 — Classification
3 — Resampling Methods
4 — Subset Selection
5 — Shrinkage
6 — Dimension Reduction
7 — Nonlinear Models
8 — Tree-Based Methods
9 — Support Vector Machines
10 — Unsupervised Learning

Question 32

What is  Linear Regression?

Answer 32

a method to predict a target variable by fitting the best linear relationship between the dependent and independent variable

Question 33

How Tree-Based Methods work?

Answer 33

• • both regression and classification problems.

- - segmenting the predictor space into a number of simple regions.

Question 34

Why Lasso is better than Ridge regression?

Answer 34

Lasso regression also performs variable selection (s=1 OLS regression,, as s small it penalize the small variance predictors”

Question 35

Data scientists live at the intersection of

Answer 35

coding, statistics, and critical thinking

Question 36

What is Hierarchical clustering?

Answer 36

builds a multilevel hierarchy of clusters by creating a cluster tree.

Question 37

What is k-Means clustering?

Answer 37

partitions data into k distinct clusters based on distance to the centroid of a cluster.

Question 38

what is Resampling?

Answer 38

is the method that consists of drawing repeated samples from the original data samples.

Question 39

Statistical Learning problems

Answer 39

• Identify the risk factors for prostate cancer.

* Establish the relationship between salary and demographic variables in population survey data.