Key Terminologies of AIRA
Modified on: Tue, 25 Jan, 2022 at 6:05 PM
Accuracy
| It is the number of correct predictions divided by the total number of predictions made. Accuracy highlights the rate of accurate model prediction. |
Accuracy Metrics
| Accuracy metrics indicate numerical values that evaluate the performance of the model.
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Data validation
| It is to check the accuracy and quality of the source data before training. It ensures that the mistakes are addressed and not silently ignored.
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Dataset
| A set of data available in a tabular format is called a dataset. It is the set of data processed in a machine to make the predictions.
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F1 score
| It is the harmonic mean of precision and recall, taking both metrics into account.
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Machine Learning Operations (MLOps)
| MLOps is a set of best practices that seamlessly brings data science solutions into existing systems. Enterprises can set up efficient data science solutions without disturbing their current set up.
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Machine learning model
| A machine learning model is a mathematical representation of a set of data and is used to make predictions/inferences. There are several types of machine learning models available and the models offer unique advantages for different use cases.
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Model training
| A data science model needs to be trained with available data to make effective predictions on newer data. A machine learning algorithm learns from a dataset and predicts results for a different set of data.
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Precision
| The ability of a classification model to identify only the relevant data points.
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Re-training
| Any model will decay as time elapses and new data keeps coming in. Consequently, deployed models would need retraining at certain intervals. Re-training with newer data increases the prediction accuracy in the longer run.
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Recall
| The ability of a model to find all the relevant cases within a dataset.
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