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Evaluation metrics for machine learning projects

Top 5 Model Evaluation Metrics For Machine Learning Projects

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Top 5 Model Evaluation Metrics For Machine Learning Projects

A crucial phase in the machine learning pipeline is data preparation and model training, but it’s equally crucial to evaluate the effectiveness of the learned model. Machine learning models are either adaptable or non-adaptive based on how effectively they generalize to new input.

Before we put our model into production on hypothetical data, we should be able to increase its overall prediction power by evaluating its performance using a variety of criteria.

 

Confusion Matrix

It is a common way of presenting true positive (tp), true negative (TN), false positive (FP), and false negative (fn) predictions. Those values are presented in the form of a matrix where the Y-axis shows the true classes while the X-axis shows the predicted classes. It is calculated on class predictions, which means the outputs from your model need to be thresholded first.

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Type II Error

A type II error is also known as a false negative and occurs when a classification model incorrectly predicts a false outcome for an originally true observation. When we don’t predict something when it is, we are contributing to the false negative rate. You can think of it as a fraction of missed fraudulent transactions that your model lets through.

 

Accuracy

Now, the above three metrics discussed are General-purpose metrics irrespective of the kind of training and test data that you have and the kind of classification algorithm you have deployed for your problem statements.

 

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Precision

There are many cases in which classification accuracy is not a good indicator of your model performance. One of these scenarios is when your class distribution is imbalanced (one class is more frequent than others). In this case, even if you predict all samples as the most frequent class, you would get a high accuracy rate, which does not make sense at all (because your model is not learning anything, and is just predicting everything as the top class).

 

Recall

The recall is another important metric, which is defined as the fraction of samples from a class that is correctly predicted by the model. More formally:

Recall=True_Positive/ (True_Positive+ False_Negative)

Therefore, for our example above, the recall rate of cat and non-cat classes can be found as:

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Recall_cat=90/100=90%

Recall_NonCat=940/1000=94%

The post Top 5 Model Evaluation Metrics for Machine Learning Projects appeared first on Analytics Insight.

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