#machine-learning

• 23 Dec 2024 · Study

  Machine Learning · Hyperparameter search

  Tuning with GridSearchCV and RandomizedSearchCV, nested evaluation on a held-out test set, and why random search often wins.

• 13 Dec 2024 · Study

  Machine Learning · Feature engineering

  Creating better inputs: ratios, date parts, binning, interactions, log transforms for skewed values, and target-free encodings.

• 5 Dec 2024 · Study

  Machine Learning · scikit-learn pipelines

  Combining preprocessing and model with Pipeline and ColumnTransformer for mixed numeric and categorical data, so one object is fitted, tuned and saved.

• 27 Nov 2024 · Study

  Machine Learning · Cross-validation

  k-fold, stratified and grouped cross-validation, why one train/test split is noisy, and avoiding leakage by preprocessing inside the folds.

• 19 Nov 2024 · Study

  Machine Learning · Model evaluation metrics

  Confusion matrix, accuracy and why it fails on imbalanced data, precision, recall, F1, ROC-AUC, and regression metrics.

• 11 Nov 2024 · Study

  Machine Learning · PCA

  Principal component analysis: rotating data onto directions of maximum variance, explained variance ratio, and compressing the digits dataset.

• 1 Nov 2024 · Study

  Machine Learning · Hierarchical clustering and DBSCAN

  Two alternatives to k-means: agglomerative clustering, which builds a tree of merges, and DBSCAN, which finds dense regions of any shape and labels noise.

• 24 Oct 2024 · Study

  Machine Learning · k-means clustering

  Unsupervised clustering with k-means: the assign/update loop, inertia, choosing k with the elbow and silhouette score.

• 16 Oct 2024 · Study

  Machine Learning · Support vector machines

  SVMs find the widest-margin boundary; support vectors, the C parameter, and the kernel trick for non-linear data.

• 8 Oct 2024 · Study

  Machine Learning · Boosting and XGBoost

  Boosting builds shallow trees one after another, each fixing the previous errors; gradient boosting, learning rate, early stopping, and XGBoost.

• 30 Sept 2024 · Study

  Machine Learning · Random forest

  Bagging many decorrelated trees: bootstrap samples, random feature subsets, out-of-bag score, and feature importances.

• 20 Sept 2024 · Study

  Machine Learning · Decision trees

  How a decision tree chooses splits using Gini impurity, why deep trees overfit, and reading the learned rules.

• 12 Sept 2024 · Study

  Machine Learning · k-nearest neighbours

  k-NN classifies by majority vote of the closest training points; why scaling matters and how k controls the bias–variance trade-off.

• 4 Sept 2024 · Study

  Machine Learning · Regularisation: ridge and lasso

  Overfitting with too many features, and how L2 (ridge) shrinks weights while L1 (lasso) drives some exactly to zero.

• 27 Aug 2024 · Study

  Machine Learning · Logistic regression

  Logistic regression for binary classification: the sigmoid, log-loss, decision thresholds, and predicted probabilities.

• 19 Aug 2024 · Study

  Machine Learning · Gradient descent

  Fitting linear regression by gradient descent from scratch: the gradient of MSE, the learning rate, and what happens when it is too small or too big.

• 9 Aug 2024 · Study

  Machine Learning · Linear regression

  Linear regression with many features: the model, the squared-error cost, the normal equation, and scikit-learn's LinearRegression with RMSE and R².

• 1 Aug 2024 · Study

  Machine Learning · The ML workflow

  The end-to-end supervised learning loop: define the task, split data, pick a baseline, fit, evaluate on held-out data, iterate.