Samy Baladram

MODEL EVALUATION & OPTIMIZATION When all models have similar accuracy, now what? Samy Baladram · Follow Published in Towards Data Science · 12 min read · 13 hours ago — You’ve trained several classification models, and they all seem to be performing well with high accuracy scores. Congratulations! But hold on — is one model truly better than the others? Accuracy alone doesn’t tell the whole story. What if one model consistently overestimates its confidence,

MODEL EVALUATION & OPTIMIZATION 12 must-know methods to validate your machine learning Samy Baladram · Follow Published in Towards Data Science · 25 min read · 13 hours ago — Every day, machines make millions of predictions — from detecting objects in photos to helping doctors find diseases. But before trusting these predictions, we need to know if they’re any good. After all, no one would want to use a machine that’s wrong most of

MODEL VALIDATION & OPTIMIZATION Stop using moving boxes to explain cross-validation! Samy Baladram · Follow Published in Towards Data Science · 10 min read · 13 hours ago — You know those cross-validation diagrams in every data science tutorial? The ones showing boxes in different colors moving around to explain how we split data for training and testing? Like this one: Have you seen that? Image by author. I’ve seen them too — one too

ENSEMBLE LEARNING Fitting to errors one booster stage at a time Samy Baladram · Follow Published in Towards Data Science · 11 min read · 9 hours ago — Decision Tree Regressor, Explained: A Visual Guide with Code Examples Trimming branches smartly with Cost-Complexity Pruning towardsdatascience.com Of course, in machine learning, we want our predictions spot on. We started with simple decision trees — they worked okay. Then came Random Forests and AdaBoost, which did

ENSEMBLE LEARNING Putting the weight where weak learners need it most Samy Baladram · Follow Published in Towards Data Science · 11 min read · 10 hours ago — Random Forest, Explained: A Visual Guide with Code Examples Making tree-mendous predictions with random trees towardsdatascience.com Everyone makes mistakes — even the simplest decision trees in machine learning. Instead of ignoring them, AdaBoost (Adaptive Boosting) algorithm does something different: it learns (or adapts) from these mistakes

REGRESSION ALGORITHM Gliding through points to minimize squares Samy Baladram · Follow Published in Towards Data Science · 11 min read · 6 hours ago — When people start learning about data analysis, they usually begin with linear regression. There’s a good reason for this — it’s one of the most useful and straightforward ways to understand how regression works. The most common approaches to linear regression are called “Least Squares Methods” — these work

DATA PREPROCESSING 10 sneaky ways your preprocessing pipeline leaks Samy Baladram · Follow Published in Towards Data Science · 13 min read · 3 hours ago — In my experience teaching machine learning, students often come to me with this same problem: “My model was performing great — over 90% accuracy! But when I submitted it for testing on the hidden dataset, it is not as good now. What went wrong?” This situation almost always

DATA PREPROCESSING Artificially generating and deleting data for the greater good Samy Baladram · Follow Published in Towards Data Science · 9 min read · 15 hours ago — ⛳️ More DATA PREPROCESSING, explained: · Missing Value Imputation · Categorical Encoding · Data Scaling · Discretization ▶ Oversampling & Undersampling Collecting a dataset where each class has exactly the same number of class to predict can be a challenge. In reality, things are rarely perfectly

DATA PREPROCESSING 6 fun ways to categorize numbers into bins! Samy Baladram · Follow Published in Towards Data Science · 10 min read · 2 hours ago — ⛳️ More DATA PREPROCESSING, explained: · Missing Value Imputation · Categorical Encoding · Data Scaling ▶ Discretization · Over- & Under-sampling (soon!) Most machine learning model requires the data to be numerical — all object or categorical data has to be in numerical format first. But, actually,

CLASSIFICATION ALGORITHM Bell-shaped assumptions for better predictions Samy Baladram · Follow Published in Towards Data Science · 9 min read · 2 hours ago — ⛳️ More CLASSIFICATION ALGORITHM, explained: · Dummy Classifier · K Nearest Neighbor Classifier · Bernoulli Naive Bayes ▶ Gaussian Naive Bayes · Decision Tree Classifier · Logistic Regression · Support Vector Classifier · Multilayer Perceptron (soon!) Building on our previous article about Bernoulli Naive Bayes, which handles binary data, we