Data World

Machine Learning For Beginners 1: Must Know Terminologies

Posted by Pramod Singla on September 26, 2018


Data model: Data model involves providing an ML algorithm (that is, the learning algorithm) with training data to learn from. The term ML model refers to the model artifact that is created by the training process.

Data Leakage: Dictionary meaning of leakage is “deliberate disclosure of confidential information”. So, data leakage means leaking of some data to your training model which can lead to over-fitting. e.g.

  • Including feature as label in model training

  • Including test data into training data

  • Distorting information from samples outside of scope of the model’s intended use.

  • Include Information from data samples outside of scope of the algorithm’s intended use.

Details ref1, ref2,ref3, ref4

Features: Features are the variables found in the given problem set that can strongly/sufficiently help us build an accurate predictive model.

Data Label vs Feature : Feature is input; label is output.

Cross validation:A mechanism for estimating how well a model will predict to new data by testing the model against one or more non-overlapping data subsets withheld from the training set.

Over-fitting vs Under-fitting vs ideal fit a model

Variance Vs Bias :error(X) = noise(X) + bias(X) + variance(X). Details

bias(X): Learning wrong things. Away from accuracy. Under-fitting.

variance(X): Learning random things.Over fitting.

False Positive vs False Negative: false positive is an outcome where the model incorrectly predicts the positive class. And a false negative is an outcome where the model incorrectly predicts the negative class. Details.

Model parameter vs Model hyper-parameter:A model parameter is a configuration variable that is internal to the model and whose value can be estimated from data.Whereas, A model hyper-parameter is a configuration that is external to the model and whose value is usually set by the data scientist. Details

Google ML Glossary

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