Day #50 - Recommendation Systems

Recommendation Systems

• Content Based
• Collaborative (User-User / Item-Item)

Content Based Key Features - Able to recommend based on user taste and historical behavior. No need of other user data
Pros 

• No Need of Data from Other users
• Recommend New and Unpopular items

Cons

• Finding Appropriate feature is hard
• Unable to Exploit Quality judgment from other users

Collaborative Key Features - Recommendation based on similar users / similar items. 
Item-Item 

• For item-i, find other similar items
• Better than user-user
• Need enough users data
• Works on any kind of item no feature selection is needed

User-User

• Find users who have bought / rated similar items
• Hard to find users rated same items

More Advanced Methods - Latent Factor Models

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Day #49 - Clustering Key Notes

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Day #48 - Distance Measures in Simple Steps

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Day #47 - Deep Dive - Learning's

Tip #1 - Support Vector Machines

• Performs classification by obtaining and utilizing optimal separating hyperplane that separates two classes and maximizes the distance to the closest point from either class called margin
• Training involves non-linear optimization
• Objective function is convex
• So, the solution to optimization problem is relatively straight forward

Tip #2 Regularization - Involves adding penalty term in Error function. Two types of regularization in linear regression

• Ridge
• Lasso

Tip #3 - Stochastic Gradient Descent

• Also called as batch gradient descent
• One example at a time, move at once
• Cheaper computation
• Randomization - Escape shallow valleys, local minima, does take care of escaping silly local minima
• Simplest possible optimization
• SGD is applied in Neural Networks

Tip #4 - Gradient Descent

• Meant to minimize non-linear function
• Error measure convex function
• Finding local minimum
• Initialize -> Iterate until termination ->Adjust Learning Rate -> Terminate on local minimum
• Return Weights

Tip #5 - Bias and Variance

• Models with two few parameters may lead to High Bias
• Models with too many parameters are inaccurate due to Large Variance

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Day #46 - Recursive Feature Elimination

Recursive feature elimination is step wise backward feature elimination.

Backward Search

• Start with all features
• Greedily remove the least relevant feature
• Stop when selected the least number of features

Recursive Feature Elimination

• Train SVM
• Rank the Features
• Eliminate Feature with lowest Rank
• Repeat until required number of features are retained

For each iteration RFE eliminates one feature with minimum weight. Intuition is feature with minimum weight would least influence weight vector form.

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Day #45 - Handling Imbalanced Classes

• SMOTE - Synthetic minority over sampling technique
• Sampling with Replacement
• Sampling without Replacement
• Under sampling of Majority Class, Oversampling of Minority Class
• Collect more samples

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Day #44 - Real time and Batch Analytics - Vendors - Stack Comparison

Summary of analysis after evaluating different stacks

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Day #43 - Random Forest - One Page Summary

Consider how the mighty random forest...[From linkedin Post]
1. Handles both classification and regression.
2. Works with both categorical and numeric data.
3. Doesn't require centering/scaling of numeric data.
4. Is robust to outliers and over-fitting.
5. Works well on many business problems with hyperparameter default values.
6. Estimates generalization error.
7. Provides insights into feature importance.
8. Can be trained in parallel.
9. Provides an intuitive vehicle for understanding and working the bias-variance trade-off.
10. Supports problems with complex decision boundaries elegantly. 

Happy Learning!!!