Machine Learning
  • Introduction
  • Self LEarning
  • Why Statstics in ML Or Data Science
  • How important is interpretability for a model in Machine Learning?
  • What are the most important machine learning techniques to master at this time?
  • Learning
    • Supervised Learning
      • Evaluating supervised learning
        • K-fold cross validation
        • Using train/test to prevent overfitting of a polynomial regression
      • Regression
        • Linear regression
          • The ordinary least squares technique
          • The gradient descent technique
          • The co-efficient of determination or r-squared
            • Computing r-squared
            • Interpreting r-squared
          • Assumptions of linear regression
          • Steps applied in linear regression modeling
          • Evaluation Metrics Linear Regression
          • p-value
        • Ridge regression
        • Least absolute shrinkage and selection operator (lasso) Regression
        • Polynomial regression
        • Performance Metrics
        • Regularization parameters in linear regression and ridge/lasso regression
        • Comments
      • Classification
        • test
        • Logistic Regression
        • naïve Bayes
        • support vector machines (SVM)
        • decision trees
          • Split Candidates
          • Stopping conditions
          • Parameters
            • Non Tunable Or Specificable
            • Tunable
            • Stopping Parameters
        • Evaluation Metrics
      • Random Forest
        • Logistic Regression Versus Random Forest
        • Paramters
          • Non Tunable Parameters
          • Tunable
          • Stopping Param
        • Parameter Comparison of Decision Trees and Random Forests
        • Classification and Regression Trees (CART)
        • How random forest works
        • Terminologies related to random forest algorithm
        • Out-of-Bag Error
      • Decision Trees
        • Gini Index
    • Unsupervised learning
      • Clustering
        • test
        • KMeans Clustering
          • Params
          • Functions
        • Gaussian Mixture
          • Parameters
          • functions
    • Semi-supervised learning
    • Reinforcement learning
    • Learning Means What
    • Goal
    • evaluation metrics
      • Regression
        • MSE And Root Mean Squared Error (RMSE)
        • Mean Absolute Error (MAE)
      • Model Validation
        • test
      • The bias, variance, and regularization properties
        • Regularization
          • Ridge regression
        • Bias And Variance
      • The key metrics to focus
    • hyperparameters
  • Steps in machine learning model development and deployment
  • Statistical fundamentals and terminology
  • Statistics
    • Measuring Central Tendency
    • Probability
    • Standard Deviation , Variance
    • root mean squared error (RMSE)
    • mean Absolute Error
    • explained Variance
    • Coefficient of determination R2
    • Standard Error
    • Random Variable
      • Discrete
      • Continuous
    • Sample vs Population
    • Normal Distribution
    • Z Score
    • Percentile
    • Skewness and Kurtosis
    • Co-variance vs Correlation
    • Confusion matrix
    • References
    • Types of data
      • Numerical data
        • Discrete data
        • Continuous data
      • Categorical data
      • Ordinal data
    • Bias versus variance trade-off
  • Spark MLib
    • Data Types
      • Vector
      • LabeledPoint
      • Rating
      • Matrices
        • Local Matrix
        • Distributed matrix
          • RowMatrix
          • IndexedRowMatrix
          • CoordinateMatrix
          • BlockMatrix
    • Comparing algorithms supported by MLlib
      • Classification
    • When and why should you use MLlib (versus scikit-learn versus TensorFlow versus foo package)
    • Pipeline
    • References
    • Linear algebra in Spark
  • Terminology
  • Machine Learning Steps
    • test
  • Preprocessing and Feature selection techniues
  • The importance of variables feature selection/attribute selection
    • Feature Selection
      • forward selection
      • mixed selection or bidirectional elimination
      • backward selection or backward elimination
      • The key metrics to focus on
  • Feature engineering
  • Hyperplanes
  • cross-validation
  • Machine learning losses
  • When to stop tuning machine learning models
  • Train, validation, and test data
  • input data structure
  • Why are matrices/vectors used in machine learning/data analysis?
    • Linear Algebra
  • OverView
  • Data scaling and normalization
  • Questions
  • Which machine learning algorithm should I use?
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  1. Learning
  2. Supervised Learning
  3. Regression

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PreviousRegularization parameters in linear regression and ridge/lasso regressionNextClassification

Last updated 5 years ago

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In linear regression, only the residual sum of squares (RSS) is minimized, whereas in ridge and lasso regression, a penalty is applied (also known as shrinkage penalty) on coefficient values to regularize the coefficients with the tuning parameter λ.

When λ=0, the penalty has no impact, ridge/lasso produces the same result as linear regression, whereas λ -> ∞ will bring coefficients to zero:

Linear regression