Performance Metrics

-

Performance metrics provide insights into how well a model is performing.

Suppose you have following Model,





Confusion Matrix:



TP : True Positive
FP : False Positive
FN : False Negative
TN : True Negative

So, for above example, confusion matrix will look like:

  1. Accuracy (ACC): ACC measures the ratio of correctly predicted instances to the total instances: ACC=��+����+��+��+��


    = 4/8
    ~ 50%

  2. Precision (PREC): PREC measures the ratio of correctly predicted positive observations to the total predicted positives. [From predicted +ve, see Actual +ve]: PREC=����+��


    ex, spam cassification

  3. Recall (Sensitivity): RECALL measures the ratio of correctly predicted positive observations to all actual positives [From Actual +ve, see predicted +ve]: RECALL=����+��

    ex, has cancer or not

  4. F Score: F Score is the harmonic mean of PREC and RECALL, balancing both metrics: ex, "Tomorrow stock market will crash", has different answer from company and user pov. So, we can't make decision for Precision & Recall. In this case, we use F score, F-Beta
    F    β​    =    (1+β2)×(β2×Precision)+RecallPrecision×Recall​

         Beta = 1, indicates F1 Score. F1 Score=2×����×����������+������

  5. If FP is more important than FN, decrease Beta value. beta=0.5 F0.5 Score If FN is more important than FP, increase Beta value. beta = 2 F2 Score 5. Specificity (SPEC): SPEC measures the ratio of correctly predicted negative observations to all actual negatives: SPEC=����+��

  6. ROC-AUC: ROC-AUC measures the area under the Receiver Operating Characteristic (ROC) curve, indicating how well the model distinguishes between classes. A higher AUC suggests a better model. For more details, go through below link : https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc

  7. Mean Absolute Error (MAE) and Mean Squared Error (MSE): For regression problems, MAE and MSE are used. MAE is the average of the absolute differences between predicted and actual values, while MSE is the average of the squared differences.


Comments

Post a Comment

Popular posts from this blog

Extracting Tables and Text from Images Using Python

-
Image
In this blog, we'll explore a complete Python solution that detects and extracts tables and text from images using libraries like Transformers, OpenCV, PaddleOCR, and easyOCR. This step-by-step breakdown includes code to detect tables, extract content from individual table cells, and retrieve any remaining text in the image. Overview When working with scanned documents, such as invoices or forms, it is essential to accurately extract both structured information (like tables) and unstructured text. The approach we’ll explore uses Microsoft's pretrained object detection model to locate tables and OCR techniques to extract the text from both table cells and the rest of the image. Steps: 1. This code first detects table using microsoft's model. and save that image which contains detected table only  2. After that, from the detected table , we make a seperate image for each cell.  3. Then we read text from the image of each cell  4. Now, to read the extra texts except for the ...
Read more

Getting Started with ML

-
Image
 Types of ML :- 1) Supervised ML :- Before going for supervised learning, you need to be aware of two terminologies: Suppose, you have the data of student's age and weight. Now, for new student, just by knowing age, you want to predict his weight. So, in this case i. Dependent Variable : Age ii. Independent Variable : Weight Regression : It is applied on Numeric Data such as temperature, weight etc. Classification : It is applied on Categorial Data. There are 2 types of classification: 1.Binary Classification - Yes/No, Spam/Not Spam, etc. 2. Multiclass Classification - Small/Medium/Large, Bad/Average/Good/Excellent etc. 2) Unsupervised ML :- i. Clustering : If we have some data where there is no dependent/independent variable, then we can use clustering. suppose, we have data for salary and age. There is no relation between salary and age. So, clustering groups the data and keep the data with same characteristics together. ii. Dimensionality Reduction : Suppose, we have 100...
Read more

Linear Regression

-
Image
 Linear Regression shows the Linear Relationship between dependent features(Y-axis) and independent features(X-axis).  There are 2 types of Linear Regression: 1. Simple Linear Regression -  It has only one independent variable. Hypothesis Function for Simple Linear Regression : where: Y is the dependent variable X is the independent variable  β0 is the intercept ( the value where the regression line crosses the y-axis) β1 is the slope/weight ex : calculate weight based on age. In above figure Red dots represent Actual Data Points and black dots represent predicted values. error is calculated by (Actual Value - Predicted Value) 2. Multiple Linear Regression -  It has more than one independent features.  Hypothesis Function for Simple Linear Regression : y= a0 + a1x1 + a2x2 + a3x3 + ..  Let's understand this by example, we all know that if the size of the house is bigger, then the price also increases. But along with this, there are many other factors ...
Read more