Day 5 - Performance metrics in Machine Learning - Classification

-

    By Jerin Lalichan 


Performance metrics in ML

    Evaluation of the performance of a model is important. Performance metrics are certain measures to quantify the performance of the model during the training and testing phases. In Machine learning, there are generally two kinds of performance metrics in use. For regression models and for classification models. Below are the most popular metrics in use:

Classification Metrics

  1. Confusion Matrix (Not a metric but base to others)




It is a visualization of ground truth vs predicted values, in the form of a matrix. It is not exactly a performance metric but forms a basis for other metrics. Each cell consists of one term, which is an evaluation factor. 
  1. TP
    This indicates how many positive cases are predicted correctly

  2. FP
    This indicates the number of cases in which the value is actually negative but predicted as positive.     This factor represents Type-I error in statistics.

  3. FN
    This indicates the values which are actually positive but predicted as negative
    This factor represents Type-II error in statistics.

  4. TN
    This indicates the number of correct negative predictions


2. Accuracy



It is the simplest metric of all. It is the ratio of total correct predictions to the total number of predictions.


3. Precision




It is the ratio of true positives and total positives predicted. Here cost of acting is High. An example of a case where precision is important is Spam classification.

4. Recall



A Recall is essentially the ratio of true positives to all the positives in the ground truth. Here cost of acting is low. An example of a case where Recall is important is cancer prediction. 
  

5. F1-Score



The F1-score metric uses a combination of precision and recall. In fact, the F1 score is the harmonic mean of Recall and Precision. Now, a high F1 score symbolizes high precision as well as high recall. It presents a good balance between precision and recall and gives good results on imbalanced classification Problems.


6. AUROC (Area under Receiver operating characteristics curve)



It is generally known as AUC-ROC curves. It uses True positive rates and false positive rates.



TPR represents how many positives are guessed correctly. FPR represents how many negatives are mistakenly considered as positives.
To combine the FPR and the TPR into a single metric, we first compute the two metrics with many different thresholds for the logistic regression, then plot them on a single graph. The resulting curve is called the ROC curve, and the metric we consider is the area under this curve, which we call AUROC.
An excellent model has AUC near to 1 which means it has a good measure of separability. A poor model has an AUC near 0 which means it has the worst measure of separability. And when AUC is 0.5, it means the model has no class separation capacity.




   
  I am doing a challenge - #66DaysofData  in which I will be learning something new from the Data Science field for 66 days, and I will be posting daily topics on my LinkedIn, On my GitHub repository, and on my blog as well.


Stay Curious!  





Comments

  1. Kale Co JakimSeptember 19, 2026 at 10:25 PM

    The article provides a clear introduction to performance evaluation for machine learning classification models. The explanation begins with the confusion matrix and its four components—TP, FP, FN, and TN—before moving to accuracy, giving readers a useful foundation for understanding how predicted values are compared with actual values.

    The distinction between the confusion matrix and performance metrics is particularly helpful, as the article explains that the matrix forms the basis for other evaluation measures. The discussion of correct positive and negative predictions and the different types of prediction errors provides useful context for learners working through a Machine Learning Course.

    ReplyDelete
  2. Kale Co JakimSeptember 19, 2026 at 10:25 PM

    The article also introduces accuracy as the ratio of correct predictions to the total number of predictions. These concepts provide a practical starting point for understanding model evaluation and can be connected with implementation-focused Machine Learning Projects for Final Year.

    ReplyDelete

Post a Comment

Popular posts from this blog

Day 22 - Seaborn - Line Plot, Barplot

-
Image
     By  Jerin Lalichan  Line Plot      The line plot is one of the most basic plots in the seaborn library. This plot is mainly used to visualize the data in the form of some time series, i.e. in a continuous manner.   Barplot      A barplot is basically used to aggregate the categorical data according to some methods and by default, it’s the mean. It can also be understood as a visualization of the group by action. To use this plot we choose a categorical column for the x-axis and a numerical column for the y-axis, and we see that it creates a plot taking a mean per categorical column.
Read more

Day 25 - Seaborn - Violinplot

-
Image
         By  Jerin Lalichan  Violinplot     It is similar to the boxplot except that it provides a higher, more advanced visualization and uses the kernel density estimation to give a better description of the data distribution.            A violin plot plays a similar role as a box and whisker plot. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Unlike a box plot, in which all of the plot components correspond to actual data points, the violin plot features a kernel density estimation of the underlying distribution.      This can be an effective and attractive way to show multiple distributions of data at once, but keep in mind that the estimation procedure is influenced by the sample size, and violins for relatively small samples might look misleadingly smooth.
Read more