Apr 4 17 tweets 33 min read
2/16

"roc_auc_score" is defined as the area under the ROC curve, which is the curve having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python
4/16

AUC is equivalent to the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics
5/16

A model whose predictions are 100% wrong has an AUC of 0. and one whose predictions are 100% correct has an AUC of 1

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI
6/16

In other words - roc_auc_score coincides with “the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one”.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics
7/16

AUC is scale-invariant. It measures how well predictions are ranked, rather than their absolute values.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI
8/16

AUC is classification-threshold-invariant. It measures the quality of the model's predictions irrespective of what classification threshold is chosen.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data
10/16

Scale invariance is not always desirable. e.g, sometimes we really do need well calibrated probability outputs, and AUC won’t tell us about that.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data
11/16

Classification-threshold invariance is not always desirable, in cases where there are wide disparities in the cost of false negatives vs. false positives, it may be critical to minimize one type of classification error.

#DataScience #MachineLearning #100DaysOfMLCode
12/16

e.g, in email spam detection, you likely want to prioritize minimizing false positives (i.e. an Email is NOT spam, but its positively determined as a spam and hence moved to span folder).

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Statistics
13/16

Even if that results in a significant increase of false negatives. (An email is indeed spam, but model determines it to be negative, i.e. Not-Spam). AUC isn't a useful metric for this type of optimization.

#DataScience #MachineLearning #Statistics #DeepLearning
14/16

How to use the AUC ROC curve for the multi-class model ?

In a multi-class model, we can plot the N number of AUC ROC Curves for N number classes using the One vs Rest methodology.

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics
15/16

“One vs Rest” is a method to evaluate multiclass models by comparing each class against all the others at the same time. Here we take one class and consider it as our “positive” class, while all the others (the rest) are considered as the “negative” class.

#DataScience
16/16

e.s. if you have three classes named X, Y, and Z, you will have one ROC for X classified against Y and Z, another ROC for Y classified against X and Z, and the third one of Z classified against Y and X.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

• • •

Missing some Tweet in this thread? You can try to force a refresh

This Thread may be Removed Anytime!

Twitter may remove this content at anytime! Save it as PDF for later use!

# More from @paulr_rohan

Apr 4
2/n
Following tips may boost model performance across different network structures with up to 5% (mAP or mean Average Precision) without increasing computational costs in any way.

#computervision #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #AI
3/n
Visually Coherent Image Mix-up for Object Detection. This has already been proven to be successful in lessening adversarial fears in network classification after testing it on COCO 2017 and PASCAL datasets with YOLOv3 models.
#computervision #pytorch
Nov 24, 2021
2/n
Types of magic commands

Line magics - starts with % character. Rest of the line is its argument passed without parentheses or quotes.

Cell magics - %% - can operate on multiple lines below their call.
#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist
Nov 22, 2021
2/n

GauGAN2 combines segmentation mapping, inpainting and text-to-image generation in a single model

#Computervision #AI #ArtificialIntelligence #TensorFlow #PyTorch #DeepLearning #DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Mathematics
3/n

Unlike GauGAN1 the GauGAN2 can translate natural language descriptions into landscape images. Typing a phrase like “sunset at a beach” generates the scene

#Computervision #AI #ArtificialIntelligence #TensorFlow #PyTorch #DeepLearning #DataScience #MachineLearning #Math
Nov 21, 2021
Nov 20, 2021
2/16
roc_auc_score is defined as the area under the ROC curve, having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Math
Nov 19, 2021
Image interpolation occurs when you resize or distort your image from one pixel grid to another.

1/n

#computervision #IMAGE #DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #programming #Math #Stat #dataviz #DataAnalytics #AI #ArtificialIntelligence #data
Image interpolation works in two directions, and tries to achieve a best approximation of a pixel's intensity based on the values at surrounding pixels.

2/n

#computervision #IMAGE #DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #programming #Math #Stat
Image resizing is necessary when you need to increase or decrease the total number of pixels, whereas remapping can occur when you are correcting for lens distortion or rotating an image.

3/n
#computervision #DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python