- Artificial Intelligence
- Data
- Data Science
- Features
- Generative AI
- Machine Learning
-
Modeling
- Autopilot Mode
- Classification
- Confusion Matrix
- Cross-Validation
- Deep Learning Algorithms
- Machine Learning Model
- Machine Learning Model Accuracy
- Machine Learning Model Deployment
- Model Blueprint
- Model Fitting
- Model Interpretability
- Model Tuning
- Multiclass Classification
- Neural Network
- Open Source Model Infrastructure
- Overfitting
- Regression
- Training Sets, Validation Sets, and Holdout Sets
- Underfitting
- Predictions
- View global site search results
- A
- AI Engineer
- AI Observability
- AIOps
- Artificial Intelligence Wiki
- Automated Machine Learning
- Autopilot Mode
- B
- Big Data
- C
- Citizen Data Scientist
- Classification
- Cognitive Computing
- Confusion Matrix
- Cross-Validation
- D
- Data Collection
- Data Governance
- Data Insights
- Data Management
- Data Preparation
- Data Profiling
- Data Science
- Deep Learning Algorithms
- E
- Explainable AI
- F
- Feature Engineering
- Feature Impact
- Feature Selection
- Feature Variables
- G
- Generative AI
- L
- Large Language Model Operations (LLMOps)
- M
- Machine Learning
- Machine Learning Algorithms
- Machine Learning Life Cycle
- Machine Learning Model
- Machine Learning Model Accuracy
- Machine Learning Model Deployment
- Machine Learning Operations (MLOps)
- Model Blueprint
- Model Fitting
- Model Interpretability
- Model Monitoring
- Model Tuning
- Multiclass Classification
- N
- Natural Language Processing
- Neural Network
- O
- Open Source Model Infrastructure
- Overfitting
- P
- Prediction
- Prediction Explanations
- Predictive Maintenance
- Production Model Governance
- Production Model Lifecycle Management
- R
- Regression
- S
- Scoring Data
- Semi-Supervised Machine Learning
- Stacked Predictions
- Supervised Machine Learning
- T
- Target Leakage
- Target Variable
- Text Mining
- Training Sets, Validation Sets, and Holdout Sets
- U
- Underfitting
- Unsupervised Machine Learning
- W
- What is Artificial Intelligence (AI)?
- View global site search results
- Artificial Intelligence
- Data
- Data Science
- Features
- Generative AI
- Machine Learning
-
Modeling
- Autopilot Mode
- Classification
- Confusion Matrix
- Cross-Validation
- Deep Learning Algorithms
- Machine Learning Model
- Machine Learning Model Accuracy
- Machine Learning Model Deployment
- Model Blueprint
- Model Fitting
- Model Interpretability
- Model Tuning
- Multiclass Classification
- Neural Network
- Open Source Model Infrastructure
- Overfitting
- Regression
- Training Sets, Validation Sets, and Holdout Sets
- Underfitting
- Predictions
Machine Learning Algorithms
What are Machine Learning Algorithms?
Algorithms are step-by-step computational procedures for solving a problem, similar to decision-making flowcharts, which are used for information processing, mathematical calculation, and other related operations.
Machine learning relies on algorithms to build models that reveal patterns in data, which in turn allow businesses to uncover insights and make predictions to improve operations, better understand customers, and solve other business problems. There are many different algorithms, but most data scientists rely on a small set with which they are familiar.
Why are Machine Learning Algorithms Important?
Algorithms are the heart of machine learning solutions. Data scientists use complex algorithms as building blocks for more efficient logical problem-solving. These algorithms take a lot of time and skill to produce, but without them, we wouldn’t have basic math, much less the ability to identify which families are likely best suited to become foster parents.
Machine Learning Algorithms + DataRobot
With traditional data science methods, running a single algorithm can be prohibitively difficult and time-consuming and may include complicated and technical data science processes like feature engineering. The DataRobot AI platform automates the model building process and runs dozens of models in parallel. The platform increases model diversity and decreases the time it takes to build models, reducing time-to-value and resource requirements of machine learning projects.