Evaluation in AI
Evaluation in AI Class 10 Notes
What is evaluation in AI?
Evaluation is the process of understanding the reliability of any AI model. It is an integral part of the model development process. It helps to find the best model that represents our data and how well it work in the future.
What is data set in Machine Learning?
Data which is used to train the machine learning model is called the data set.
What is Overfitting in Machine Learning?
Overfitting in machine learning occurs when the machine learning model gives accurate predictions for training data but not for new data.
What is Scenario in Machine Learning?
A scenario can be defined as one or more particular case or situation on which we want to run machine learning algorithms. Let we understand with an example of the Forest fire scenario.
Imagine that you have an AI based prediction model which has been deployed in a forest which is prone to forest fires. Now, the objective of the model is to predict whether a forest fire has broken out in the forest or not.
To understand the efficiency of this model, we need to check whether the predictions made by this model are correct or not.
Now let us look at various combinations that we can have with Prediction (The prediction is the output which is given by the machine) and Reality (the reality is the real scenario which happened in reality).
The Prediction matches with the Reality. Hence, this condition is termed as True Positive.
In this case both prediction and reality matches ie NO Therefore, this condition is termed as True Negative
Here the machine has incorrectly predicted that there is a forest fire. This case is termed as False Positive.
The above case shows the False Negative
What is Confusion Matrix in Machine Learning?
The result of comparison between the prediction and reality can be recorded in a matrix called the confusion matrix. The confusion matrix allows us to understand the prediction results.
Let us now take a look at the confusion matrix:
Prediction and Reality can be easily mapped together with the help of this confusion matrix.
Evaluation Methods/Parameters to check a machine learning model’s Performance
There are many evaluation methods/parameters which are used to evaluate machine learning model’s performance. Let we discuss few
1. Accuracy : Accuracy is defined as the percentage of correct predictions out of all the observations. A prediction can be said to be correct if it matches the reality.
Accuracy = Total Correct Predictions/Total Cases *100
Correct Predictions = True Positive (TP) + True Negative (TN)
Total Predictions = True Positive (TP) + True Negative (TN) + False Positive (FP) + False Negative (FN)
So the formula for accuracy becomes:
Accuracy = (TP + TN)/(TP +TN +FP +FN) *100
2. Precision : Precision is defined as the percentage of true positive cases versus all the cases where the prediction is true.
Precision = True Positive/Total Predicted Positive * 100
Precision = TP/(TP + FP) * 100
3. Recall : It can be defined as the fraction of positive cases that are correctly identified. It considers True Positive and False Negative.
Recall = TP/(TP + FN)
4. F1 Score : F1 score can be defined as the measure of balance between precision and recall.
F1 Score = 2 * (Precision * Recall)/(Precision + Recall)
An ideal situation would be when we have a value of 1 (that is 100%) for both Precision and Recall. In that case, the F1 score would also be an ideal 1 (100%). It is known as the perfect value for F1 Score.
In conclusion, we can say that a model has good performance if the F1 Score for that model is high.
Disclaimer : I tried to give you the correct “Evaluation in AI NOTES” , but if you feel that there is/are mistakes in “Evaluation in AI NOTES” given above, you can directly contact me at csiplearninghub@gmail.com. The above “Evaluation in AI NOTES” are created for practice of students and the entire content is from CBSE Study material. Screenshots used in above article “Evaluation in AI NOTES” is taken from CBSE study material.
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