Day #39 - Useful Tool MyMediaLite for Recommendations

This post is based on learning's for assignment link1, link2

Input is User-Items file as listed below

Sample Execution Command

We will be supplying parameter 20 in user20.txt to identify recommendations for user 20. The recommender type is mentioned in the --recommender parameter

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Day #38 - Python Matrix Operations Learnings

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Day #37 - Numpy Learnings - Matrices

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Day #36 - Pandas Dataframe Learning's

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Day #35 - Bias Vs Variance

These are frequently occurring terms with respect to performance of model against training and testing data sets.

Classification error = Bias + Variance

Bias (Under-fitting)

• Bias is high if the concept class cannot model the true data  distribution well, and does not depend on training set size.
• High Bias will lead to under-fitting

How to identify High Bias

• Training Error will be high
• Cross Validation error also will be high (Both will be nearly the same)

Variance(Over-fitting)

• High Variance will lead to over-fitting

How to identify High Variance

• Training Error will be high
• Cross Validation error also will be Very Very High compared to training error

Hot to Fix ?
Variance decreases with more training data, and increases with more complicated classifiers

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Day #34 - What is diffference between Logistics Regression and Naive Bayes

Both are probabilistic
Logistics

• Discriminative (Entire approach is purely discriminative)
• P(Y/X)
• Final Value lies between Zero and 1
• Formula given by exp(w0+w1x)/(exp(w0+ w1x)+1)
• Further can be expressed as 1/(1+(exp-(w0+ w1x))

Binary Logistic Regression - 2 class

Multinomial Logistic Regression - More than 2 class

Example - Link

Link - Ref
Logistic Regression

• Classification Model
• Probability of success as a sigmoid function of a linear combination of features
• y belongs to (0,1) - 2 Class problem
• p(yi) = 1 / 1+e-(w1x1+w2x2)
• Linear combination of features - w1x1+w2x2
• w can be found with max likelihood estimate- 

Naive Bayes

• Generative Model
• P(X/ Given Y) is Naive Bayes Assumption
• Distribution for each class

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Day #33 - Pandas Deep Dive

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Good Data Science Course Links

AI Lectures

Introduction to Machine Learning

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Short Analytics Concept Videos

• Descriptive Analysis (Analysis of existing data, Trends and Patterns), 
• Diagnostic Analysis (Reasons / Patterns behind events)
• Predictive Analytics (Future how will it look like) 
• Prescriptive Analysis (How to be prepared / handle the future)

Great Compilation, Keep Learning!!!

Day #32 - Regularization in Machine Learning

A large coefficient will result in overfitting. To avoid we perform regularization. Regularization - To avoid overfitting

• L1 - Sum of values (Lasso - Least absolute shrinkage and selection operator). L1 will be meeting in co-ordinates and result in one of the dimensions zero. This would result in variable elimination. The features that minimally contribute will be ignored.
• L2 - Sum of squares of values (Ridge). L2 is kind of circle shaped. This will shrink all coefficient in same proportion but eliminate none
• Discriminative - In SVM we use hyperplane to classify the classes. This is example for discriminative approach
• Probabilistic - Generated by Gauss Distribution. This is again based on Central Limit Theorem. Infinite points will fit into a Normal distribution. Here we apply gauss distribution model
• Max Likelihood - Probability that the point p belongs to one distribution. 

Good Read for L2 - Indeed, using the L2 loss comes from the assumption that the data is drawn from a Gaussian distribution

Another Read -

• L1 Loss function minimizes the absolute differences between the estimated values and the existing target values. L1 loss function is more robust and is generally not affected by outliers
• L2 loss function minimizes the squared differences between the estimated and existing target values. L2 error will be much larger in the case of outliers 

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