IA e Machine Learning
Machine learning, geração aumentada por recuperação (RAG), aplicações de IA e web crawling.
Lagrangian Duality and the KKT Conditions
The primal view trains SVMs by gradient descent, but it hides two treasures: why support vectors appear, and how the kernel trick becomes possible. Both emerge when we rewrite the SVM through Lagrangian duality. This is
Ler artigo →The Kernel Trick
Here is the idea that turned a linear classifier into one of the most powerful tools in machine learning. A linear SVM can only draw straight boundaries — useless for data shaped like rings or spirals. The kernel trick l
Ler artigo →Kernels in Depth
The kernel is where you inject knowledge about your data's structure. Choosing and tuning it is the single most consequential decision in applying an SVM. This chapter surveys the standard kernels, explains the crucial p
Ler artigo →Support Vector Regression
The maximum-margin idea is not limited to classification. Support Vector Regression (SVR) flips the logic: instead of keeping points outside a margin, it tries to keep them inside a tube around the prediction, ignoring s
Ler artigo →Solving SVMs: Optimization Algorithms
An SVM is only as useful as our ability to solve its optimization problem efficiently. The dual is a quadratic program with n variables and a dense n× n matrix — naively intractable for large data. This chapter explains
Ler artigo →Multiclass, Probabilities, and Practical SVMs
An SVM is natively a binary, non-probabilistic classifier. Real problems have many classes and often need probabilities. This chapter covers the multiclass schemes, how to extract calibrated probabilities, and the end-to
Ler artigo →Margins, VC Dimension, and Generalization
Why does maximizing the margin actually help on unseen data? The answer is one of the crown jewels of machine learning: statistical learning theory, developed largely by Vapnik alongside the SVM. This chapter explains VC
Ler artigo →SVMs versus the World: When to Use What
When should you reach for an SVM today? This chapter places SVMs honestly in the modern landscape — against logistic regression, tree ensembles, and deep networks — with a clear-eyed account of their strengths, weaknesse
Ler artigo →Support Vector Machines in Machine Learning, Deep Learning, and AI
This capstone gathers the threads. It is tempting to file SVMs under “what we used before deep learning,” but that misses the deeper story: the ideas SVMs crystallized — maximum margin, kernels, convex duality, generaliz
Ler artigo →Algorithm and Hyperparameter Reference
A compact reference for the formulas, algorithms, and knobs used throughout the book.
Ler artigo →What Are Tree-Based Methods?
A decision tree is the most human of machine-learning models: it is a flowchart of yes/no questions, exactly how a doctor triages a patient or a loan officer screens an application. This chapter introduces the core idea
Ler artigo →Decision Trees for Classification
To grow a classification tree we need a precise answer to one question: what makes a split good? The answer is purity — a good split produces child nodes whose samples are as close to a single class as possible. This cha
Ler artigo →Decision Trees for Regression
Trees predict numbers as easily as labels. A regression tree partitions the feature space the same way, but each leaf predicts a constant value — the average target in that region — and splits are chosen to reduce varian
Ler artigo →Growing, Splitting, and Pruning
A tree left to grow freely will keep splitting until every leaf is pure — memorizing the training set, noise and all. The art of a single good tree is controlling its size: how to grow it, when to stop, and how to cut it
Ler artigo →Strengths, Weaknesses, and the Bias–Variance of Trees
Before we multiply trees into forests and boosters, it pays to understand a single tree's character honestly: what it does brilliantly, where it fails, and — most importantly — the bias–variance behavior that motivates e
Ler artigo →Bagging and Random Forests
We now turn high-variance single trees into one of the most robust models in machine learning. The recipe has two ingredients: bagging (train many trees on bootstrap resamples and average them) and the random forest twis
Ler artigo →Boosting: AdaBoost and Gradient Boosting
Bagging builds trees in parallel to cut variance. Boosting builds them in sequence to cut bias: each new tree focuses on what the ensemble got wrong so far. This chapter develops boosting from AdaBoost (reweight the mist
Ler artigo →Modern Gradient Boosting: XGBoost, LightGBM, CatBoost
Friedman's gradient boosting is the idea; XGBoost, LightGBM, and CatBoost are the engineering that made it dominate. Each adds algorithmic and systems innovations — regularized objectives, second-order optimization, hist
Ler artigo →Feature Importance and Interpretability
A single tree is a readable flowchart, but a forest of hundreds or a thousand boosted trees is a black box. The good news: tree ensembles support some of the best interpretability tools in machine learning. This chapter
Ler artigo →Practical Tree Modeling
Knowing the algorithms is half the battle; the other half is the craft of applying them to messy real data. This chapter is a practitioner's checklist: handling categoricals and missing values, dealing with class imbalan
Ler artigo →Specialized and Advanced Trees
Beyond classification, regression, forests, and boosting lies a rich ecosystem of tree variants tuned for special jobs: detecting anomalies, ranking search results, modeling time-to-event, quantifying uncertainty, and sq
Ler artigo →Trees versus the World: When to Use What
When should you reach for a tree ensemble, and when for something else? This chapter places trees in the broader modeling landscape — against linear models and against deep learning — and explains the inductive biases th
Ler artigo →Tree-Based Methods in Machine Learning, Deep Learning, and AI
This capstone gathers the threads. Tree-based methods are not a quaint pre-deep-learning relic — they are a living, dominant branch of modern AI, and increasingly they converge with deep learning. We tour their reign ove
Ler artigo →Tree-Based Methods — Preface: how to use this book
There is a quiet truth in applied machine learning: while deep networks dominate images, audio, and text, the workhorse for the tables that run businesses — spreadsheets of customers, transactions, sensors, and records —
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