You may train your machine learning models for months in a row. However, you cannot know the model is useful unless you evaluate your machine learning model accuracy. Here we have discussed how to assess your ML model accuracy.
Evaluation metrics to consider
F1 Score – According to experts like https://logicplum.com/knowledge-base/machine-learning-model-accuracy, the F1 score is used to measure test accuracy. The F1 score is generally a harmonic mean between recall and precision. It can tell how robust your classifier is and how many instances it classifies accurately. Lower recall and higher precision can give you accurate results. Still, it misses a large number of instances that cannot be classified.
Mean Squared Error – It takes the square of the difference between predicted values and original values. In MSE, you can compute gradients much more efficiently than Mean Absolute Error. Since it takes squares of the error, it helps you determine the effect of larger errors. You can focus on them to achieve better accuracy.
Mean Absolute Error – It refers to the average of predicted values and original values. The Mean Absolute Error helps you know the difference between predicted values from the actual output. One drawback of Mean Absolute Error is – it does not give you the exact direction of error. In simple words, it does not tell whether you are under-predicting or over-predicting.
Confusion Matrix – As per experts at https://logicplum.com/knowledge-base/machine-learning-model-accuracy/, it gives you the ML model’s complete performance. You get the matrix as output. The confusion matrix is binary and based on prediction and actual results. The following terms explain the confusion matrix in short.
- True Positives– when you predicted a Yes in a case and the output is also Yes.
- True Negatives– When you predicted a Yes in a case but the output in No
- False Positives– when you predicted a No in a case, but the output is Yes.
- False Negatives– when you predicted a No in a case and the output is also No.
Classification Accuracy – refers to the ratio of the total number of input samples and the number of correct predictions. The formula of classification accuracy is
- Accuracy = number of correct predictions/total number of accurate predictions
Classification accuracy gives you a false sense of attaining higher accuracy, which cannot be guaranteed. The real problem is the number of minor samples’ misclassification is relatively high that can affect the results.
The Machine Learning Model Accuracy evaluation can be divided into three parts – baseline model skill, determining the best score, and discovering the limits of your model skill.
Baseline model skill
The difficulty in achieving accuracy in the machine learning model is relative, and it has not been solved before. The model depends on your domain knowledge, but there is no guarantee the skill scores can be achieved through the ML model.
The baseline model skill is based on well-understood and straightforward procedures. They are used for prediction in defining predictive modeling problems. The baseline model skill output can become a base point or foundation on which all ML models can be trained, and data can be evaluated.
Determining the best score
The best score depends on the type of predictive modeling problem. For the classification problem, the best score of the model is 100% accuracy. For the regression problem, the best score of the model is 0% error.
According to experts, it isn’t easy to achieve the best score in ML models. Still, you can know the best possible performance that can be achieved for a specific measure.
Limits of model skill
There are several problems in model skill, and you can consider these strategies to overcome them.
Start High – it is a fast approach that defines a model skill’s bounds on a particular problem. The Start High strategy can help you determine whether you can solve the problem fast.
To sum up, these are some ways you can evaluate your machine learning model accuracy.
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