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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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  • Technical Metrics
  • Correlating to Business Metrics

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Metrics in Machine Learning

PreviousML ShowcaseNextMachine Learning Models Explained

Last updated 5 years ago

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In the context of machine learning, a metric is any number that we care about. An objective is a specific type of metric that a machine learning system attempts to optimize.

Technical Metrics

is the most common (and easy to understand) metric but tracking only accuracy will paint an incomplete picture of how your model is performing. There are several other well-established metrics that provide deeper insight into model performance.

Metrics are often specific to the type of machine learning problem or model. Important and widely adopted metrics include: Accuracy, , , , Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R Square.

Correlating to Business Metrics

Machine learning metrics are often directly correlated to business metric. One example would be assigning a dollar value to false positives in a classification model. Here's a great of how AirBnB measures the performance of their fraud prediction algorithm in dollars.

Accuracy
Loss
Confusion Matrix
AUC (Area Under ROC curve)
example