🤖
AI Wiki
Gradient PlatformDocsGet Started FreeContact Sales
  • Artificial Intelligence Wiki
  • Topics
    • Accuracy and Loss
    • Activation Function
    • AI Chips for Training and Inference
    • Artifacts
    • Artificial General Intelligence (AGI)
    • AUC (Area under the ROC Curve)
    • Automated Machine Learning (AutoML)
    • CI/CD for Machine Learning
    • Comparison of ML Frameworks
    • Confusion Matrix
    • Containers
    • Convergence
    • Convolutional Neural Network (CNN)
    • Datasets and Machine Learning
    • Data Science vs Machine Learning vs Deep Learning
    • Distributed Training (TensorFlow, MPI, & Horovod)
    • Generative Adversarial Network (GAN)
    • Epochs, Batch Size, & Iterations
    • ETL
    • Features, Feature Engineering, & Feature Stores
    • Gradient Boosting
    • Gradient Descent
    • Hyperparameter Optimization
    • Interpretability
    • Jupyter Notebooks
    • Kubernetes
    • Linear Regression
    • Logistic Regression
    • Long Short-Term Memory (LSTM)
    • Machine Learning Operations (MLOps)
    • Managing Machine Learning Models
    • ML Showcase
    • Metrics in Machine Learning
    • Machine Learning Models Explained
    • Model Deployment (Inference)
    • Model Drift & Decay
    • Model Training
    • MNIST
    • Overfitting vs Underfitting
    • Random Forest
    • Recurrent Neural Network (RNN)
    • Reproducibility in Machine Learning
    • REST and gRPC
    • Serverless ML: FaaS and Lambda
    • Synthetic Data
    • Structured vs Unstructured Data
    • Supervised, Unsupervised, & Reinforcement Learning
    • TensorBoard
    • Tensor Processing Unit (TPU)
    • Transfer Learning
    • Weights and Biases
Powered by GitBook
On this page

Was this helpful?

  1. Topics

Linear Regression

PreviousKubernetesNextLogistic Regression

Last updated 5 years ago

Was this helpful?

Linear regression is an algorithm (belonging to both statistics and machine learning) that models the relationship between two or more variables by fitting a linear equation to a dataset. Independent variables are the features (input data) and dependent variables are the target (what you are trying to predict).

The technique is very simple and can be represented by this familiar equation:

However, this is typically written slightly differently in machine learning:

Or for a more advanced model with multiple features:

Where:

  • y is the predicted label

  • b is the bias (the intercept)

  • w1 is the coefficient or weight of the first feature (weight = m or slope)

  • x1 is a feature (an input)

Linear vs Logistic Regression

Linear regression predictions are continuous (e.g. test scores from 0-100).

Logistic regression predictions classify items where only specific values or classes are allowed (e.g. binary classification or multiclass classification). The model provides a probability score (confidence) with each prediction.

Like , is typically used to optimize the values of the coefficients (each input value or column) by iteratively minimizing the loss of the model during training.

Mean squared error and mean absolute error are common for linear regression.

Regularization is a technique used to prevent by penalizing signals that provide too much explanatory power to a single feature.

logistic regression
gradient descent
loss functions
overfitting
Source: Wikipedia ’Linear Regression’