From Simplicity to Sophistication: Mastering Classical Machine Learning Models

A few years ago, I built my first churn prediction model. It was simple: a logistic regression on customer demographics and app usage. I was proud of it—until it missed half the churners. That kicked off a journey through some of the most widely used classification models today: Logistic Regression, Random Forest, XGBoost, and LightGBM.

In this comprehensive guide, I'll walk you through how each model works mathematically, when each shines, the subtle trade-offs that go beyond accuracy, and how they fit into the broader landscape of classical machine learning.

Understanding Classical ML vs Deep Learning

Before diving into specific models, it's crucial to understand where classical ML fits in the AI hierarchy:

AI ⊃ ML ⊃ Deep Learning ⊃ Specific Architectures

Classical ML algorithms (linear models, trees, SVMs, naive Bayes) differ from deep learning in several ways:

• Data Requirements: Classical ML works well with smaller datasets (hundreds to thousands of samples)
• Interpretability: Much easier to explain predictions to stakeholders
• Compute Requirements: Train on CPUs in minutes rather than GPUs for hours
• Performance Trade-offs: Deep learning excels on unstructured data (images, text), classical ML on structured/tabular data

When to use Classical ML:

• Tabular data with clear features
• Limited training data (< 100K samples)
• Interpretability is critical (healthcare, finance, legal)
• Fast iteration and deployment needed
• Computational resources are limited

When to consider Deep Learning:

• Unstructured data (images, audio, text)
• Massive datasets (> 1M samples)
• Complex feature interactions difficult to engineer
• Accuracy trumps interpretability

Logistic Regression: The Trustworthy Workhorse

Logistic Regression was my first love. Clean, interpretable, and surprisingly powerful. It assumes a linear relationship between your input variables and the log-odds of the target. You can explain it to stakeholders in a single sentence: "Each feature increases or decreases the odds of churn."

Mathematical Foundation

Logistic regression uses the sigmoid function to map predictions to probabilities:

P(y=1|x) = 1 / (1 + e^(-(w·x + b)))

Where:

• w is the weight vector (coefficients)
• x is the input feature vector
• b is the bias term
• The output is a probability between 0 and 1

Loss Function: Binary Cross-Entropy (Log Loss)

Loss = -[y·log(ŷ) + (1-y)·log(1-ŷ)]

Optimization: Uses gradient descent or specialized solvers (LBFGS, Newton-CG)

Regularization Techniques

Regularization prevents overfitting by adding penalty terms:

L2 Regularization (Ridge):

• Adds penalty: λ·Σ(w²)
• Shrinks coefficients toward zero
• Keeps all features, reduces multicollinearity

L1 Regularization (Lasso):

• Adds penalty: λ·Σ|w|
• Performs feature selection (sets coefficients to exactly 0)
• Good for high-dimensional sparse data

Elastic Net:

• Combines L1 and L2: λ₁·Σ|w| + λ₂·Σ(w²)
• Balances feature selection and coefficient shrinkage

Key Assumptions

1. Linear relationship between features and log-odds
2. Independence of observations
3. No multicollinearity among features
4. Large sample size (typically > 10 events per predictor)

Why use it:

• Interpretability: Feature coefficients are easy to explain
• Probabilistic: Outputs calibrated probabilities, not just classes
• Speed: Trains in seconds, scales to millions of rows
• Baseline: Excellent first-cut model
• Mathematical Theory: Well-understood statistical properties
• Maximum Likelihood: Provides confidence intervals and p-values

Pitfalls:

• Linearity Assumption: Misses complex interactions and non-linear patterns
• Feature Engineering: Needs well-crafted variables (e.g., one-hot encoding, binning, polynomial features)
• Poor with Imbalanced Data: Needs tricks like SMOTE, class weights, or threshold tuning
• Outliers: Sensitive to extreme values
• Complete Separation: Fails when classes are perfectly separable

When it shines:

Early-stage analysis, healthcare (disease prediction), finance (credit scoring, fraud detection), legal (case outcomes), or when interpretability trumps raw accuracy. Also excellent as a baseline to beat.

Random Forest: The Reliable All-Rounder

Next came Random Forests. It felt like magic: accuracy jumped, tuning was easier, and no more worrying about multicollinearity.

Random Forests are an ensemble of decision trees, trained on random subsets of data and features. They average the predictions to reduce overfitting.

How Decision Trees Work

Before understanding forests, understand trees:

Decision Tree Algorithm:

1. Start with all data at root node
2. Find best feature and split point that maximizes information gain or minimizes Gini impurity
3. Recursively split child nodes
4. Stop when reaching max depth or min samples

Splitting Criteria:

Gini Impurity (classification):

Gini = 1 - Σ(pᵢ²)

Where pᵢ is the proportion of class i

Information Gain (based on entropy):

Entropy = -Σ(pᵢ·log₂(pᵢ))
IG = Entropy(parent) - Σ(weighted_entropy(children))

MSE (regression):

MSE = (1/n)·Σ(yᵢ - ŷ)²

Random Forest: Bagging + Randomness

Random Forest improves on single trees through:

1. Bootstrap Aggregating (Bagging):

• Sample N instances with replacement
• Train one tree per sample
• Average predictions (regression) or vote (classification)

2. Feature Randomness:

• At each split, consider only √p features (classification) or p/3 (regression)
• Reduces correlation between trees
• Prevents a few strong features from dominating

3. Ensemble Prediction:

Classification: ŷ = mode(tree₁(x), tree₂(x), ..., treeₙ(x))
Regression: ŷ = (1/n)·Σ(treeᵢ(x))

Key Hyperparameters

1. n_estimators: Number of trees (more is better, but diminishing returns after ~100-500)
2. max_depth: Maximum tree depth (prevents overfitting)
3. min_samples_split: Minimum samples to split a node
4. min_samples_leaf: Minimum samples in leaf node
5. max_features: Features to consider for splitting
6. bootstrap: Whether to use bootstrap sampling

Why use it:

• Nonlinear Power: Captures complex patterns and interactions automatically
• No Assumptions: Doesn't assume linearity or normality
• Robust to Outliers: Tree splits are rank-based
• Handles Missing Data: Can work with missing values
• Low Tuning: Reasonable performance out-of-the-box
• Feature Importance: Good for ranking variables (mean decrease in impurity)
• Parallel Training: Trees can be trained independently

Pitfalls:

• Interpretability: Hard to explain why a prediction was made
• Speed: Slower than logistic regression, especially on large datasets
• Memory Usage: Can become heavy in production (stores all trees)
• Extrapolation: Cannot predict beyond the range of training data
• Bias on Imbalanced Data: Tends to favor majority class

When it shines:

Tabular data with interactions, moderate-size datasets (10K-1M rows), quick wins in hackathons, when you need good performance without extensive tuning, fraud detection, customer segmentation.

XGBoost: The Competitive Beast

Then came a Kaggle competition. Logistic and RF weren't cutting it. Everyone whispered: XGBoost.

XGBoost (Extreme Gradient Boosting) builds trees sequentially, where each tree corrects the previous ones' mistakes. Unlike Random Forest's bagging, this is boosting.

Gradient Boosting Fundamentals

Core Idea: Build an ensemble by sequentially adding models that predict the residuals (errors) of the previous ensemble.

Algorithm:

1. Start with initial prediction (usually mean for regression, log-odds for classification)
2. For each iteration m = 1 to M:
   • Calculate residuals: rᵢ = yᵢ - ŷᵢ
   • Train a tree to predict residuals
   • Update predictions: ŷ = ŷ + η·tree_m(x)
3. Final prediction: ŷ = Σ(η·tree_m(x))

Where η is the learning rate (typically 0.01-0.3)

XGBoost Innovations

XGBoost improves traditional gradient boosting with:

1. Regularized Objective Function:

L = Σ[loss(yᵢ, ŷᵢ)] + Σ[Ω(tree)]
Ω(tree) = γT + (λ/2)Σ(w²)

Where:

• γ controls tree complexity (L0 regularization)
• λ controls leaf weights (L2 regularization)
• T is number of leaves

2. Second-Order Optimization:

• Uses both gradient and Hessian (second derivative)
• Faster convergence than first-order methods
• More accurate approximation of the loss

3. Parallel Processing:

• Parallel tree construction (splits evaluated in parallel)
• Cache-aware block structure
• Out-of-core computation for huge datasets

4. Smart Missing Value Handling:

• Learns optimal default direction for missing values
• No need to impute beforehand

5. Built-in Cross-Validation:

• CV during training to find optimal number of boosting rounds

Key Hyperparameters

Tree Structure:

• max_depth (3-10): Maximum tree depth
• min_child_weight (1-10): Minimum sum of instance weights in child
• gamma (0-5): Minimum loss reduction to make split

Regularization:

• lambda (L2 reg): Default 1
• alpha (L1 reg): Default 0
• eta (learning rate, 0.01-0.3): Shrinkage to prevent overfitting

Sampling:

• subsample (0.5-1): Row sampling per tree
• colsample_bytree (0.5-1): Column sampling per tree

Boosting:

• n_estimators (100-1000): Number of boosting rounds
• early_stopping_rounds: Stop if no improvement

Why use it:

• Accuracy: Frequently outperforms other models (wins ~70% of Kaggle competitions)
• Regularization: Built-in L1/L2 penalties to control overfitting
• Handling Missing Data: Smart splitting logic learns optimal defaults
• Speed: Optimized C++ implementation with parallelization
• Flexibility: Custom objective functions and evaluation metrics
• Feature Importance: Multiple metrics (gain, cover, frequency)

Pitfalls:

• Tuning Hell: Many hyperparameters to get right (grid search or Bayesian optimization needed)
• Sequential Training: Trees must be built sequentially (unlike RF)
• Overfitting: If not careful with depth, learning rate, and regularization
• Sensitive to Outliers: More than Random Forest
• Memory: Requires more RAM than simpler models

When it shines:

Data science competitions (Kaggle), production ML pipelines, fraud detection, click-through rate prediction, marketing response models, ranking systems, when you need the last 1-5% accuracy improvement.

LightGBM: The Fast Learner

When speed became an issue, I discovered LightGBM. Built by Microsoft, it's optimized for performance without sacrificing accuracy.

LightGBM uses leaf-wise tree growth (vs. level-wise in XGBoost), which leads to deeper, more efficient splits.

Key Innovations

1. Leaf-wise (Best-first) Tree Growth:

• XGBoost: Level-wise (splits all nodes at same depth)
• LightGBM: Leaf-wise (splits leaf with max delta loss)
• Result: Deeper trees, lower loss, but higher overfitting risk

2. Gradient-based One-Side Sampling (GOSS):

• Keeps instances with large gradients (large errors)
• Randomly samples instances with small gradients
• Reduces data size while maintaining accuracy
• Speeds up training by focusing on "hard" examples

3. Exclusive Feature Bundling (EFB):

• Bundles mutually exclusive features (sparse features that rarely take non-zero values simultaneously)
• Reduces feature dimensionality
• Especially effective for high-dimensional sparse data

4. Histogram-based Algorithm:

• Discretizes continuous features into bins (typically 255)
• Faster than XGBoost's pre-sorted algorithm
• Lower memory usage
• Allows parallel and distributed training

5. Native Categorical Feature Support:

• Handles categorical features without one-hot encoding
• Finds optimal split for categories
• Saves memory and improves accuracy

Key Hyperparameters

Tree Structure:

• num_leaves (31 default): Max leaves in tree (key param, controls complexity)
• max_depth (-1 default): Unlimited depth
• min_data_in_leaf (20): Minimum samples per leaf

Boosting:

• learning_rate (0.1): Shrinkage rate
• n_estimators (100): Number of boosting iterations
• bagging_fraction (1.0): Row sampling ratio
• feature_fraction (1.0): Column sampling ratio

Regularization:

• lambda_l1, lambda_l2: Regularization terms
• min_gain_to_split: Minimum gain to make split

Speed Optimization:

• max_bin (255): Max number of bins for features
• num_threads: Parallel threads

Why use it:

• Speed: Trains 10-20x faster than XGBoost on large datasets
• Memory Efficiency: Lower RAM footprint (histogram-based)
• Handles Categorical Features: Can process them natively without encoding
• Large Data: Optimized for datasets > 100K rows
• Distributed Training: Easy to scale across machines
• GPU Support: Fast GPU acceleration

Pitfalls:

• Overfitting Risk: Leaf-wise growth can overfit on small datasets (< 10K rows)
• Sensitive to num_leaves: Main hyperparameter to tune carefully
• Less Transparent: Harder to interpret than trees or logistic regression
• Small Data: Use XGBoost for datasets < 10K rows

LightGBM vs XGBoost

| Aspect | XGBoost | LightGBM | | ----------- | ----------------------------- | ------------------------ | | Tree Growth | Level-wise | Leaf-wise | | Speed | Slower on large data | Faster (3-15x) | | Memory | Higher | Lower | | Small Data | Better | Risk overfitting | | Large Data | Good | Excellent | | Categorical | Manual encoding | Native support | | Accuracy | Slightly better on small data | Comparable on large data |

When it shines:

Large-scale classification (> 100K rows, > 100 features), ranking systems (learning-to-rank), recommendation systems, time-sensitive pipelines, click-through rate prediction, ad tech, when speed is critical.

CatBoost: The Categorical Specialist

CatBoost (Categorical Boosting), developed by Yandex, is another gradient boosting variant that excels at handling categorical features.

Key Features

1. Ordered Boosting:

• Addresses prediction shift problem in gradient boosting
• Uses different permutations of data for building trees
• Reduces overfitting, especially on small datasets

2. Native Categorical Handling:

• Uses target statistics with advanced encoding
• Automatically handles categorical features
• Greedy target-based statistics to prevent overfitting

3. Symmetric Trees (Oblivious Trees):

• Same split criterion across entire tree level
• Faster inference
• Better regularization
• Less prone to overfitting

When to use CatBoost:

• Many categorical features (10+ categorical columns)
• Small to medium datasets (CatBoost often wins on < 100K rows)
• When you want good results with minimal tuning
• Ranking problems
• Time series with categorical metadata

CatBoost vs XGBoost vs LightGBM

| Feature | XGBoost | LightGBM | CatBoost | | -------------------- | ------- | -------------- | ----------------- | | Categorical handling | Manual | Native (basic) | Native (advanced) | | Small data | Good | Overfits | Excellent | | Large data | Good | Excellent | Good | | Speed | Medium | Fast | Slowest | | Tuning needed | High | Medium | Low | | Default performance | Good | Good | Excellent |

Support Vector Machines (SVM)

Before tree-based models dominated, SVMs were the go-to for many classification tasks.

How SVMs Work

Core Idea: Find the hyperplane that maximizes the margin between classes.

Mathematical Formulation:

• Find hyperplane: w·x + b = 0
• Maximize margin: 2/||w||
• Subject to: yᵢ(w·xᵢ + b) ≥ 1 for all i

The Kernel Trick: SVMs can handle non-linear boundaries by mapping data to higher dimensions using kernel functions:

Linear Kernel: K(x, x') = x·x'

• Use when data is linearly separable
• Fast and interpretable

Polynomial Kernel: K(x, x') = (γx·x' + r)ᵈ

• Degree d controls complexity
• Good for polynomial decision boundaries

RBF (Radial Basis Function) Kernel: K(x, x') = exp(-γ||x - x'||²)

• Most popular kernel
• Can model complex non-linear boundaries
• γ controls influence of single training example

Sigmoid Kernel: K(x, x') = tanh(γx·x' + r)

• Similar to neural network activation

Key Hyperparameters

1. C (regularization): Trade-off between margin and misclassification

   • Large C: Hard margin (lower bias, higher variance)
   • Small C: Soft margin (higher bias, lower variance)

2. kernel: Type of kernel function

3. gamma (for RBF): Defines influence of single training example

   • Large γ: Close points have influence (overfitting risk)
   • Small γ: Far points have influence (underfitting risk)

Why use SVM:

• High-dimensional data: Works well when features > samples
• Clear margin of separation: When classes are well-separated
• Memory efficient: Uses only support vectors (subset of training data)
• Kernel flexibility: Can model complex non-linear boundaries
• Binary classification: Excellent for two-class problems

Pitfalls:

• Slow on large datasets: Training time O(n²) to O(n³)
• Kernel choice: Requires experimentation
• Hyperparameter sensitivity: C and γ need careful tuning
• No probabilistic output: Need Platt scaling for probabilities
• Multi-class: Requires one-vs-one or one-vs-all strategy

When it shines:

Text classification, image classification (before deep learning), bioinformatics, small to medium datasets (< 10K samples), high-dimensional problems.

K-Nearest Neighbors (KNN)

KNN is one of the simplest ML algorithms: classify based on k nearest training examples.

How it Works

1. Choose k (number of neighbors)
2. Calculate distance to all training points
3. Find k closest points
4. Classification: Vote by majority class
5. Regression: Average of k values

Distance Metrics:

• Euclidean: √Σ(xᵢ - yᵢ)² (most common)
• Manhattan: Σ|xᵢ - yᵢ|
• Minkowski: Generalization of above
• Cosine: For high-dimensional sparse data

Why use KNN:

• No training phase: Lazy learning (stores all data)
• Simple and interpretable: Easy to understand
• No assumptions: Non-parametric
• Naturally handles multi-class: No special strategy needed

Pitfalls:

• Slow prediction: Must compute distance to all training points
• Memory intensive: Stores entire training set
• Curse of dimensionality: Performance degrades in high dimensions
• Sensitive to scale: Needs feature normalization
• Sensitive to k: Needs cross-validation to choose k
• Imbalanced data: Majority class dominates

When it shines:

Small datasets, recommendation systems (collaborative filtering), anomaly detection, as a baseline model, when training time isn't critical.

Naive Bayes

Naive Bayes applies Bayes' theorem with the "naive" assumption that features are independent.

Bayes' Theorem

P(Class|Features) = P(Features|Class) × P(Class) / P(Features)

Naive Assumption: Features are conditionally independent given the class

P(x₁, x₂, ..., xₙ|Class) = P(x₁|Class) × P(x₂|Class) × ... × P(xₙ|Class)

Variants

Gaussian Naive Bayes:

• Assumes features follow normal distribution
• For continuous features
• P(xᵢ|Class) = (1/√(2πσ²)) × exp(-(xᵢ-μ)²/(2σ²))

Multinomial Naive Bayes:

• For discrete count features (word counts, frequencies)
• Text classification

Bernoulli Naive Bayes:

• For binary features (word presence/absence)
• Document classification

Why use Naive Bayes:

• Fast: Training and prediction are extremely fast
• Low data requirements: Works with small datasets
• Scalable: Handles large feature spaces well
• Probabilistic: Provides probability estimates
• Multi-class: Natural multi-class classifier
• Text classification: Excels at spam detection, sentiment analysis

Pitfalls:

• Independence assumption: Rarely true in reality
• Zero frequency problem: Needs Laplace smoothing
• Poor probability estimates: Though classification is often accurate
• Continuous features: Gaussian assumption may not hold

When it shines:

Text classification (spam detection, sentiment analysis), document categorization, real-time prediction systems, when you need a fast baseline, small datasets.

Ensemble Methods: Combining the Best

Beyond individual models, ensemble methods combine multiple models for better performance.

Voting Classifiers

Hard Voting: Majority vote Soft Voting: Average predicted probabilities

# Combine Logistic Regression, Random Forest, SVM
Prediction = mode(LR, RF, SVM)  # Hard voting
Prediction = argmax(avg(P_LR, P_RF, P_SVM))  # Soft voting

Stacking (Stacked Generalization)

Idea: Train a meta-model on predictions of base models

Algorithm:

1. Split data into train/holdout
2. Train base models on train set
3. Generate predictions on holdout set
4. Train meta-model on these predictions
5. For new data: base models → meta-model

Base models: Diverse models (LR, RF, XGBoost, SVM) Meta-model: Often logistic regression or neural network

Blending

Similar to stacking but simpler:

• Use separate validation set for meta-model
• Less prone to overfitting than stacking

Why use ensembles:

• Improve accuracy: Often outperform single models
• Reduce variance: Averaging reduces overfitting
• Robustness: Less sensitive to outliers or noise
• Capture diverse patterns: Different models learn different aspects

Pitfalls:

• Complexity: Harder to deploy and maintain
• Training time: Multiple models to train
• Interpretability: Very difficult to explain
• Diminishing returns: Each additional model adds less value

When to use:

Competitions, when accuracy is paramount, when you have time for experimentation, production systems with sufficient resources.

The Takeaway: Know Your Tradeoffs

Comprehensive Model Comparison

| Model | Data Size | Interpretability | Speed | Accuracy | Hypertuning | Best Use Case | | ----------------------- | ------------ | ---------------- | ----- | -------- | ----------- | ------------------------------------- | | Logistic Regression | Small-Large | ★★★★★ | ★★★★★ | ★★☆☆☆ | ★★★★☆ | Baselines, regulated industries | | Naive Bayes | Small-Medium | ★★★★☆ | ★★★★★ | ★★☆☆☆ | ★★★★★ | Text classification, real-time | | KNN | Small | ★★★★☆ | ★☆☆☆☆ | ★★★☆☆ | ★★★☆☆ | Small datasets, recommendations | | SVM | Small-Medium | ★★☆☆☆ | ★★☆☆☆ | ★★★★☆ | ★★☆☆☆ | High-dimensional, text classification | | Decision Tree | Small-Medium | ★★★★★ | ★★★★☆ | ★★☆☆☆ | ★★★☆☆ | Explainable decisions | | Random Forest | Medium-Large | ★★☆☆☆ | ★★★☆☆ | ★★★★☆ | ★★★★☆ | General tabular data | | XGBoost | Medium-Large | ★★☆☆☆ | ★★★☆☆ | ★★★★★ | ★★☆☆☆ | Competitions, max accuracy | | LightGBM | Large | ★★☆☆☆ | ★★★★★ | ★★★★★ | ★★★☆☆ | Large-scale, speed critical | | CatBoost | Small-Large | ★★☆☆☆ | ★★★☆☆ | ★★★★★ | ★★★★☆ | Many categoricals, ease-of-use | | Ensemble | Any | ★☆☆☆☆ | ★☆☆☆☆ | ★★★★★ | ★☆☆☆☆ | Competitions, max performance |

Decision Tree: Choosing the Right Model

START
│
├─ Need interpretability?
│  ├─ YES → Logistic Regression or Decision Tree
│  └─ NO → Continue
│
├─ What data size?
│  ├─ Small (< 10K) → KNN, SVM, or CatBoost
│  ├─ Medium (10K-100K) → Random Forest or XGBoost
│  └─ Large (> 100K) → LightGBM or XGBoost
│
├─ What data type?
│  ├─ Text → Naive Bayes, SVM, or Logistic Regression
│  ├─ Many Categoricals → CatBoost
│  ├─ High-dimensional → SVM or Logistic Regression with L1
│  └─ Tabular numeric → Gradient Boosting (XGBoost/LightGBM)
│
├─ Speed critical?
│  ├─ Training speed → Logistic Regression, Naive Bayes
│  ├─ Inference speed → Logistic Regression, LightGBM
│  └─ Both → Logistic Regression
│
├─ Need probabilities?
│  ├─ Well-calibrated → Logistic Regression, Naive Bayes
│  └─ Just predictions → Any model
│
└─ Maximum accuracy?
   └─ Ensemble (Stacking/Blending) > XGBoost/LightGBM > Random Forest

Key Principles for Model Selection

1. Start Simple

• Always begin with Logistic Regression or Random Forest
• Establish a baseline to beat
• Understand your data before complex models

2. Match Model to Problem

• Interpretability needed? → Linear models, single trees
• Tabular data? → Tree-based models (RF, XGBoost, LightGBM)
• Text data? → Naive Bayes, Logistic Regression, SVM
• Small dataset? → Simpler models (avoid deep learning)
• Imbalanced data? → Adjust class weights, use appropriate metrics

3. Consider the Full Pipeline

• Deployment: Can you serve the model in production?
• Maintenance: Can your team retrain and monitor it?
• Latency: What's the acceptable inference time?
• Resources: GPU/CPU requirements?

4. Don't Overfit the Benchmark

• A model that's 1% better on validation but takes 10x longer to train may not be worth it
• Production impact > Leaderboard position

5. Feature Engineering Still Matters

• Good features + simple model often beats bad features + complex model
• Tree models need less feature engineering than linear models
• Deep learning needs the least feature engineering (learns features)

Common Mistakes to Avoid

❌ Using complex models without trying simple ones first

• Start with logistic regression, then iterate

❌ Not handling class imbalance

• Use stratified splits, class weights, SMOTE, or appropriate metrics

❌ Overfitting on validation set

• Don't tune hyperparameters on test set
• Use cross-validation for robust estimates

❌ Ignoring data leakage

• Ensure temporal ordering for time series
• Don't include future information in features

❌ Choosing model based on accuracy alone

• Consider precision/recall trade-offs
• Factor in interpretability, speed, maintainability

❌ Not normalizing features for distance-based models

• KNN and SVM require feature scaling
• Tree-based models don't

The Journey Forward

Classical ML isn't dead—it's the foundation. While deep learning dominates headlines, tree-based models still win most tabular data competitions and power countless production systems.

Master the classics first:

1. Understand the math (loss functions, optimization, regularization)
2. Build intuition (bias-variance, overfitting, feature importance)
3. Practice on real problems (Kaggle, work projects)
4. Then explore deep learning

Don't let hype decide your model. Let your data size, business goal, interpretability need, and time constraints lead the way.

Sometimes the humble logistic regression is all you need. Sometimes you need gradient boosting. And sometimes, the real trick is just knowing which is which.

The best model is not the most sophisticated one—it's the one that solves the business problem effectively while being maintainable in production.