The Machine Learning project is basically a series of steps. My point is data is the key to any ML project. And the Terminology people call is something different in the context of ML. So I am sharing the useful Machine learning Dataset terminology. Here is a Quiz on Machine Learning.
Sample ML Input 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 |
In the above table, 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.
ML Dataset Terminology.
Word | Definition |
---|---|
instance or example | A single object, observation, transaction, or record. |
target or label | The numerical or categorical (label) attribute of interest. This is the variable to be predicted for each new instance. |
features | The input attributes that are used to predict the target. These also may be numerical or categorical. |
model | A mathematical object describing the relationship between the features and the target. |
training data | The set of instances with a known target to be used to fit an ML model. |
recall | Using a model to predict a target or label. |
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. |
unsupervised machine learning | Machine-learning techniques that don’t rely on labeled examples, but rather try to find hidden structure in unlabeled data. |
ML workflow | The stages in the ML process: data preparation, model building, evaluation, optimization, and prediction. |
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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