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These are the questions from my last article on Decision Trees -
These are the questions from my last article on Decision Trees -
- Instability - what if I make small changes to my data, will it be prone to a significant change in its predictions?
- Accuracy - is one tree enough? Do we need a forest?
- Bias - what if it is biased towards beaches, how do you manage bias in information gain in classification problems?
And here are the answers -
And here are the answers -
- Instability - Decision trees can be unstable with small changes in data, addressed by ensemble methods like Random Forests.
- Accuracy - One tree might not be sufficient due to potential overfitting; Random Forests (and other ensembles) typically provide better accuracy by reducing variance.
- Bias in Information Gain - Manage bias by normalizing or weighting information gain calculations, or using alternative splitting criteria like Gini index or gain ratio.
One of the best practices in Data Science is ensuring Balanced Data Before Training.
One of the best practices in Data Science is ensuring Balanced Data Before Training.
Issue? Imbalanced data can throw shade on your model's predictions.
Issue? Imbalanced data can throw shade on your model's predictions.
Imagine a dataset where 90% of the labels are "Sunny" and only 10% are "Rainy".
Imagine a dataset where 90% of the labels are "Sunny" and only 10% are "Rainy".
Your model might just be stuck on "Sunny" all day to hit high accuracy!
Your model might just be stuck on "Sunny" all day to hit high accuracy!
Solution? Use techniques like oversampling, undersampling, or SMOTE (Synthetic Minority Over-sampling Technique) to balance your dataset.
Solution? Use techniques like oversampling, undersampling, or SMOTE (Synthetic Minority Over-sampling Technique) to balance your dataset.
This ensures your model doesn't play favorites and delivers reliable predictions across all scenarios.
This ensures your model doesn't play favorites and delivers reliable predictions across all scenarios.
Remember, balanced data is the key to unlocking powerful insights and building robust machine learning models.
Remember, balanced data is the key to unlocking powerful insights and building robust machine learning models.
Stay balanced and keep cleaning those datasets!
Stay balanced and keep cleaning those datasets!
Get in touch at jain.van@northeastern.edu
Get in touch at jain.van@northeastern.edu
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