# Imbalanced Data — Oversampling Using Gaussian Mixture Models

## Other generative models can also similarly be used for oversampling

• Introduction
• Dataset preparation
• Intro to GMM
• Using GMM as an oversampling technique
• Evaluation of performance metrics
• Conclustion

## Introduction

In a previous article, I discuss how one can come up with many creative oversampling techniques that can outperform SMOTE variants. We saw how oversampling using “crossovers” outperformed SMOTE variants.

## Dataset preparation

We first generate…

## Lookahead mechanisms in decision trees can produce better predictions

Suppose we are trying to predict if a potential job candidate can be successful in his job.

# Correlated Variables in Monte Carlo Simulations

## Can sales of vanilla ice cream overtake chocolate?

• Problem Statement
• Data preparation
• Wrong method 1 — Independent simulation (parametric)
• Wrong method 2 — Independent simulation (non-parametric)
• Method 1 — Multivariate distribution
• Method 2— Copulas with marginal distributions
• Method 3— Simulating historical combinations of sales growth
• Method 4— Decorrelating store sales growth using PCA

## Introduction

Monte Carlo simulation is a great forecasting tool for sales, asset returns, project ROI, and more.

# Imbalanced Data — Oversampling Using Genetic Crossover Operators

## Crossover/recombination oversampling adds novelty to a dataset and can score well on classification metrics vs. SMOTE and random oversampling

• Introduction
• Dataset preparation
• Random oversampling and SMOTE
• Crossover oversampling
• Evaluation of performance metrics
• Conclusion

## Introduction

Many of us have been in the situation of working on a predictive model with an imbalanced dataset.

• Oversampling techniques
• Undersampling techniques
• Combinations of over and under…

# Intro to Monte Carlo Simulation Using Business Examples

## Example 1: Sales Offer From a Wholesaler

Suppose you have an innovative product that you have been selling for the past year.

# Interpretable Models — How Linear Regression Outperforms Boosted Trees on Sanity Checks

## Tree-based ensembles and other popular algorithms often lead to counter-intuitive predictions when kept unchecked

• Intro to model controllability
• Preparing a sample dataset (House Sales in King County, USA)
• Finding the model with the top cross-validation score (CatBoost)
• Linear model’s outperformance in sanity checks
• Conclusion

## Introduction

Gradient boosted trees have been widely used to win several competitions on Kaggle. It is no surprise that for most tabular datasets you are working with, you would likely find XGBoost or another implementation of boosted decision trees as the model with the best cross-validation score on your metric(s).

## Bassel Karami

Leading a data science team building retail analytics for shopping malls in the MENA region. MSc Econometrics | CFA, FRM, and CMA.

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