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My Data Science Notes
Random Forest Classifier
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    • Welcome!
    • 01 The Basics
    • 02 Math
    • 03 Data Collection
    • 04 Feature Engineering
    • 05 Data Visualization
    • 06 Machine Learning
    • 07 Deep Learning
    • 08 Big Data Technologies
    • 09 Ethics and Bias
    • Welcome!
    • 01 The Basics
      • Overview of Common Data Science & ML Tools
      • Introduction to Data Ethics and Bias in Data Science
      • Understanding Data Types: The Foundation of Analysis
      • The Typical Data Science / Machine Learning Workflow
      • Introduction to Exploratory Data Analysis (EDA)
      • Introduction to Data Science and Machine Learning
      • Understanding the Main Types of Machine Learning
      • Comprehensive Statistics and Machine Learning Glossary
    • 02 Math
      • 01 Probability
          • Basic Probability: Definitions and Terminology
          • Types of Probability
          • Combinatorics: Counting Techniques
          • Conditional Probability
          • Independence of Events
          • Bayes' Theorem
          • Random Variables: Definition
          • Probability Mass Function (PMF)
          • Probability Density Function (PDF)
          • Cumulative Distribution Function (CDF)
          • Joint, Marginal, and Conditional Distributions
          • Moment Generating Functions (MGF)
          • Overview of Common Probability Distributions
          • Bernoulli Distribution
          • Binomial Distribution
          • Poisson Distribution
          • Uniform Distribution (Continuous)
          • Normal (Gaussian) Distribution
          • Exponential Distribution
          • Geometric Distribution
          • Negative Binomial Distribution
          • Gamma Distribution
          • Beta Distribution
          • Expected Value (Mean of a Random Variable)
          • Variance and Standard Deviation
          • Covariance and Correlation
          • Law of Large Numbers (LLN)
          • Central Limit Theorem (CLT)
      • 02 Inferential statistics
        • 001 Inferential Statistics
        • 002 Degrees of Freedom
        • 003 Central Limit Theorem
        • Hypothesis testing
        • Statistics
        • Topics
            • Guassian Distribution
            • Central Tendency
            • Mean
            • Median
            • Mode
            • Measures of variability
            • Standard Deviation
            • Variance
          • Standard error
          • Bias
          • Tradeoffs
          • Variance
          • Drawing 2023 02 09 22.08.03.excalidraw
          • Anova.excalidraw
          • Hyp testing map.excalidraw
          • T test.excalidraw
          • Two 18.42.09.excalidraw
          • Parametric Tests
            • Implementation
            • T test
            • ANOVA
            • Assumptions
            • Mechanism
      • 03 Linear Algebra
          • Scalars
          • Vectors
          • Matrices
          • Tensors
          • Vector Operations
          • Matrix Operations
          • Identity Matrix
          • Matrix Inverse
          • Determinant
          • Matrix Trace
          • Linear Independence
          • Span and Basis
          • Matrix Rank
          • Vector and Matrix Norms: Definition
          • L₁ Norm (Manhattan Norm)
          • L₂ Norm (Euclidean Norm)
          • Frobenius Norm
          • Eigenvalues and Eigenvectors
          • Singular Value Decomposition (SVD)
          • Overview of Matrix Decompositions
      • 04 Calculus
          • Functions, Limits, and Continuity
          • Derivatives
          • Rules of Differentiation
          • Chain Rule
          • Functions of Multiple Variables
          • Partial Derivatives
          • Gradient
          • Directional Derivatives
          • Maxima and Minima (Optimization Basics)
          • Gradient Descent
          • Gradient Descent Variants (SGD, Mini-batch)
          • Convexity in Optimization
          • Hessian Matrix (Placeholder)
          • Jacobian Matrix (Placeholder)
          • Lagrange Multipliers (Placeholder)
          • Definite and Indefinite Integrals (Placeholder)
          • Applications in Probability (Placeholder)
          • Matrix Calculus Essentials for ML
      • 05 Information Theory
        • Information Entropy
        • Cross-Entropy
        • KL Divergence (Kullback-Leibler Divergence)
    • 03 Data Collection
        • Categorical (Qualitative data)
        • Numeric (Quantitative data)
        • Population & Sample
        • Sampling Methods
    • 04 Feature Engineering
        • Encoding
    • 05 Data Visualization
    • 06 Machine Learning
          • Akaike Information Criterion
          • Model Selection
          • Occam’s razor principle
            • Ensemble
              • Bagging
              • Random Forest Classifier
              • Boosting
              • Gradient Boosting
              • XGBoost
              • Stacking
          • ML model
          • Model Evaluation
            • Overfitting
            • Underfitting
            • Good fit
              • Performance Metrics
                • Confusion Metrics
                • F1 Score
                • Precision
                • Recall
              • Regularization
                • Implementation
                • Lasso Regression
                • Implementation
                • Ridge Regression
          • 01 Optimizers Intro
          • Bayesian Optimization
          • Ridge Regularization
          • K fold cross validation
        • Supervised
          • Regression
            • Implementation
            • Linear Regression
              • 01 Linear relationship
              • 02 Multivariate normality
              • 03 No or little multicollinearity
              • 04 No auto correlation
              • 05 Homoscedasticity
            • Implementation
            • Polynomial Regression
              • Bayesian Linear Regression
              • Implementation
          • Classification
            • Decision Tree
            • Implementation
            • Entropy
            • Implementation
            • Logistic Regression vs Linear Regression
            • Logistic Regression
        • Unsupervised
          • Clustering
            • Exclusive Clustering
            • Implementation
            • Implementation
            • Overlapping Clustering
            • Hierarchical Clustering
            • Implementation
            • Implementation
            • Probabilistic Clustering
          • Dimensionality Reduction
          • Implementation
          • Association Rules
          • Implementation
          • Autoencoder
          • Implementation
        • Intro
        • Metric Formation Framework
        • Intro
    • 07 Deep Learning
            • 01 Introduction
            • 02 Math
            • 03 Batches, Layers & Objects
            • 04 Hidden Layer Activation Functions
            • 05 Softmax Activation
            • Implimentation incomplete
            • Transformer Networks
            • Generative Adversarial Networks (GAN)
            • Autoencoders and Variational Autoencoders (VAE)
          • Data Augmentation
          • Normalization
          • Preprocessing Pipelines
          • 01 Hyperparameter Tuning
          • 02 Optimization Algorithms
          • 03 Regularization Methods
          • 04 Transfer Learning
          • 01 Cross Validation
          • 02 Metrics
          • 03 Confusion Matrix
          • 04 ROC Curves
          • 01 Attention Mechanisms
          • 02 Self Supervised Learning
          • 03 Meta Learning
          • 04 Few Shot Learning
          • 05 Chaining
          • 06 Prompt Engineering
          • 07 Parameter Efficient Fine Tuning (PEFT)
          • 08 Retrieval Augmented Generation (RAG)
          • 09 Autonomous Agents
        • JAX
        • Keras
        • PyTorch
        • Tensor
        • TensorFlow
          • 01 Model Serving
          • 02 API Development
          • 03 MLOps
          • Implementation
          • Autonomous Vehicles
          • Finance
          • Healthcare
          • Retail
          • Untitled
    • 08 Big Data Technologies
    • 09 Ethics and Bias

    Random Forest Classifier

    Random Forest Classifier

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