Sridhar RatakondaFounder, PredixDATA, LLC

http://www.predixdata.com

Machine learning /Algorithms

& Business use

cases

What is Statistical learning?

Let’s say you want to associate sales based on advertising channel.

Input variables “Xn” => “TV budget”, “Radio budget”, “newspaper budget”

Output variable “Y” => Sales

Y = f(X) + ͼ

Statistical learning refers to set of ways for estimating “f”

Estimate of “f” / PredictionIn many situations, a set of inputs X are readily available, but the output Y cannot be easily obtained. we can predict Y using Yˆ = ˆf(X),

fˆ = estimate for f Yˆ = resulting prediction for Y

Ex: Predicting sales based on advertisement spend

Estimate of “f” / Inference 1 of 2

In some cases we want to understand how Y changes as a function of X1,...,Xp.

• Which predictors are associated with the response?• What is the relationship between the response and

each predictor?• Can the relationship between Y and each predictor

be adequately summarized using a linear equation

Estimating “f”Broadly speaking two methods are applied:

• Parametric

• Non-Parametric

Parametric models 1 of 2Parametric methods involve a three-step model-based approach.

I. First, make an assumption about shape, of f. For example, one very simple assumption is that f is linear in X: f(X) = β0 + β1X1 + β2X2 + ... + βpXp.

II. After a model has been selected, uses the training data to fit or train the model. Solve for parameters (β0, β1, …..) Y ≈ β0 + β1X1 + β2X2 + ... + βpXp.

III. Apply the model to predict on test data

Parametric models 2 of 2PROS• Fewer observations needed• Simpler to model

CONS• Not flexible

income ≈ β0 + β1 × education + β2 × seniority.

Non-Parametric models 1 of 2 Non-parametric methods do not make explicit assumptions about

the functional form of f

Instead they seek an estimate of f that gets as close to the data points as possible

Accurately fits known data (train data)

Optimized to fit existing data

High variability for true data

Non-Parametric models 2 of 2

Smooth thin-plate spline fit

Trade-Off / Prediction accuracy and Model interpretability

Supervised Vs. Unsupervised Learning Part 1 0f 3

Supervised learning

For each observation of the predictor measurement(s) xi, i = 1,...,n there is an associated response measurement yi.

linear regression, logistic regression, boosting, support vec- regression (SVM) etc.

Majority of statistical models fall under “supervised mode”

Supervised Vs. Unsupervised Learning Part 2 0f 3

Unsupervised learning

Unsupervised learning describes situation in which for every observation i = 1,...,n, we observe a vector of measurements xi but no associated response variable

No response variable to fit

Ex: Cluster analysis for customer segmentation

Unsupervised Learning - Clustering

Regression Vs. Classification

Classification model use cases

Spam Filter

Google news classification

Cancel cell classification (Benign, Malignant)

Machine learning process / Lab

Ex: Titanic Data set in KDNuggets

Lab: Titanic.R

Assessing model accuracy / Quality of fit

For regression model Numnber of test data elements

Mean Squared error

Actual valuePredicted value

Assessing model accuracy / Quality of fit

For Classification models Predicted value

Actual valueNumnber of test data elements

Top Machine learning algorithms and business use cases

Decision treesStructured way to arrive at a logical conclusion

Business use cases Option pricing Pattern recognition

“R” library -> caret

Naïve Bayes ClassificationSimple probabilistic classifiers (Baye’s theorem)

Business use cases Sentiment analysis (ex: FB

analyses status updates)

Classify spam mails

“R” library -> e1071

Simple Linear Regression

Business use cases Predicting sales Risk assessment

“R” library -> stats

Logistics Regression Modeling a binomial outcome with one or more explanatory variables

Measures the relationship between the categorical dependent variable and one or more independent variables

Business use cases Weather prediction / Credit scoring

“R” library -> MASS

Support Vector Machines (SVM)Support Vectors are co-ordinates of individual observation (ex: 45,150)

SVMis a frontier which best segregates the Male from the Females “R” library -> e1071

Random Forest When you can’t think of any algorithm use “Random Forest” “R” library -> randomForest

Simple linear regression 1 of 3Linear regression assumes that there is approximately a linear relationship between X and Y.

Y ≈ β0 + β1X (regressing Y on X)

(Ex) Sales ≈ β0 + β1 × TV

Predicted variable SlopeY intercept

Simple linear regression 2 of 3

Let

Then

additional $1,000 spent on TV advertising = approximately 47.5 additional units

Simple linear regression 3 of 3

Accuracy of estimates (standard error) 1 of 2A true relationship between Y & X takes the form

Standard error

Standard error is introduced because model is calculated using “available data” (sample data) Whole population data is not known during modeling and hence introduction of error

Accuracy of estimates (standard error) 2 of 2Standard errors can be used to compute confidence intervals

For linear regression, the 95 % confidence interval for β1, β0 approximately takes the form:

In the case of the advertising data, the 95 % confidence interval for β0 is [6.130, 7.935] and the 95 % confidence interval for β1 is [0.042, 0.053].

Interpreting standard error in regression

LAB Advertising (Summary output)

Accuracy of the model Residual Standard Error (RSE) is used to measure

accuracy of the model Roughly speaking, it is the average amount that the

response will deviate from the true regression line.

Interpreting RSE & For advertising data RSE = 3.26 i.e. 3,260 units difference in sales

Average sales = 14,000 units

%error = 3260/14000 = 23%

indicates variability of “Y” explained using “X”

ABOUT ME 25 years in Technology Industry

LinkedIn Profile: https://www.linkedin.com/in/ratakondas/

Experience working for multiple early stage startups and leading global teams

CurrentPrincipal Founder – PredixDATA(a analytics/bigdata service company)

Board of managers – Syntilla (stealth startup)