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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
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Linear Regression
Source: Wikipedia ’Linear Regression’
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)
Like logistic regression, gradient descent 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 loss functions for linear regression.
Regularization is a technique used to prevent overfitting by penalizing signals that provide too much explanatory power to a single feature.

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.
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