Here are top ten Machine Learning Terminology. Data is prime source for any ML project. And the Terminology people call is something different in the context of ML.
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Machine Learning Terms and Definitions
| Word | Definition |
|---|---|
| 1. instance or example | A single object, observation, transaction, or record. |
| 2. target or label | The numerical or categorical (label) attribute of interest. This is the variable to be predicted for each new instance. |
| 3. features | The input attributes that are used to predict the target. These also may be numerical or categorical. |
| 4. model | A mathematical object describing the relationship between the features and the target. |
| 5. training data | The set of instances with a known target to be used to fit an ML model. |
| 6. recall | Using a model to predict a target or label. |
| 7. supervised machine learning | Machine learning in which, given examples for which the output value is known, the training process infers a function that relates input values to the output. |
| 8. unsupervised machine learning | Machine-learning techniques that don’t rely on labeled examples, but rather try to find hidden structure in unlabeled data. |
| 9. ML workflow | The stages in the ML process: data preparation, model building, evaluation, optimization, and prediction. |
| 10. online machine learning | A form of machine learning in which predictions are made, and the model is updated, for each new example. |
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Machine Learning Sample Data
| Instances (Examples) | M/F (Attribute-1) | Salary (Attribute-2) | Target (Label) |
|---|---|---|---|
| instance-1 | M | 10,000 | Eligible for Bonus |
| Instance-2 | F | 20,000 | Not Eligible for Bonus |
Here, the predictable value (Eligibility) is pre-defined. So, if you know the Target upfront, it is called Supervised Learning. In the case of unsupervised learning, the target is unknown; It is something to find hidden predictions.
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