To Help C-level Leaders Stay Above the Curve
We seeks to provide leading-edge thought leadership from both internal and external senior experts and executives that help Chief Digital Officers, Chief Data Officers, Chief AI officers, and other C-level leaders navigate digital and data disruption
Insights
Becoming A Top Data Scientist In 10 Days
How To Evaluate A Neural Network Model Part I
How To Evaluate A Neural Network Model Part I
How to evaluate a neural network model? We will break this topic into two parts. Part I and Part II.
How To Evaluate A Neural Network Model Part II
How To Evaluate A Neural Network Model Part II
In Part I, we’ve talked about learning curves. While loss function can help you with model optimization, we also need to know if the model…
How To Avoid Overfitting In Neural Networks
How To Avoid Overfitting In Neural Networks
Most of the time, underfitting happens when the models or algorithms are too simple to fit more complex trends. The solution to this is…
Techniques to Combat Class Imbalance in Machine Learning
Techniques to Combat Class Imbalance in Machine Learning
Do you remember the rare disease example? It is a common problem called class imbalance. When evaluate models trained on this type of data…
Bagging, Random Forest, And Boosting
Bagging, Random Forest, And Boosting
Decision trees, bagging, random forest and boosting can all be applied to both regression and classification. Decision trees are simple to…
XGBoost, LightGBM, CatBoost
XGBoost, LightGBM, CatBoost
In the past, we did an overview of the tree-based methods. Today we will focus on three gradient boosting algorithms XGBoost, LightGBM, and…
Underfitting, Overfitting, And The Bias-Variance Tradeoff
Underfitting, Overfitting, And The Bias-Variance Tradeoff
Understanding model fit is important for understanding the root cause for poor model accuracy. The ideal scenario when fitting a model is…