TP, FP, TN, FN, Accuracy, Precision, Recall, F1-Score, Sensitivity, Specificity, ROC, AUC

Рет қаралды 25,589

ML & DL Explained

Күн бұрын

Пікірлер: 39

@lambdacalculus8316 12 күн бұрын

Thank you so much for explaining these ideas with this concise video.

@guetsenelson2792 2 ай бұрын

Thanks for taking Time and explaining so well

@namelessbecky 3 ай бұрын

Thank you. This is much understandable than my textbook

@dhandrat 2 жыл бұрын

Thankyou so much. Brilliant video !

@ml_dl_explained 2 жыл бұрын

You're most welcome

@aristotlesocrates8409 7 ай бұрын

Excellent explanation

@houstonfirefox 2 ай бұрын

Great video! Suggestion: Normalize volume to 50% going forward as I really had to crank up the speakers to hear your voice.

@thewisearchitect Жыл бұрын

Very well explained. Thank you very much. I just pressed the Subscribe button :)

@mrsmurf911 Жыл бұрын

Good Content Subscribed right away!!!

@ml_dl_explained Жыл бұрын

Thank you very much!

@KomalRathore-l6f 4 күн бұрын

Easy to understand

@snugalmond1659 Жыл бұрын

I am so grateful. Thank you.

@andrefurlan 8 ай бұрын

Thanks! More videos please!

@tesfatsion2004 3 ай бұрын

Stunning!!

@jhmrem Жыл бұрын

Great intro

@RameshChintapalli-h7n 11 ай бұрын

great explanation

@dhualshammaa2062 Жыл бұрын

its great simple video will be great to do more videos showing the over fitting and under fitting and other questions that normally been on interviews

@ml_dl_explained Жыл бұрын

Thank you for the positive feedback. I'll do my best.

@מיכלשמואלי-פ2ל 2 жыл бұрын

Great video

@ml_dl_explained 2 жыл бұрын

Thanks!

@my_master55 2 жыл бұрын

Thank you for the vid 👍 But what do you mean by "thresholds" at 11:10 ? Like, what are the thresholds in terms of neural networks, and how can we change them? Thank you :)

@ml_dl_explained 2 жыл бұрын

Thank you for the positive feedback :) The most simple way to think of a threshold is through a simple model, with only 1 feature as an input and two possible classes as output (say 0 and 1). Then when the model is trained it finds the "best" threshold for the input feature. So, for example, if we denote the input feature as a, then the model may learn that if a>=0.5 then the label is 0, otherwise it's 1. In this example the threshold is 0.5. Neural networks work differently (in most cases) and thus thinking of a threshold may be confusing. For our example from the paragraph above, the output of a neural network will be a vector/list of size 2, where each index is the probability that the output is in that specific class. For example if the output is [0.14, 0.85] then the model "thinks" that there is a 14% chance that the input is from label 0 and a 85% chance its from label 1. If our neural network had only 1 neuron then the 0.5 value from the example above could be incorporated into it. "How do we change it?" - This really depends on what you want to achieve. If FA are more important than FN, or the other way around then you can change your loss function and the incorporated threshold will change accordingly. Hope this helps :)

@my_master55 2 жыл бұрын

@@ml_dl_explained cool, thanks 👍 Tbh, I didn't really get how can we "vary the thresholds" to further plot ROC or AUC for a neural network. . I mean, when a model is trained - we have only a single point at the ROC plot (current state of the model). But then how can we "change the thresholds" to have multiple points on the plot? Thank you 😊

@ml_dl_explained 2 жыл бұрын

You're welcome :) When we train the model, it outputs probabilities. We can change the threshold of those probabilities to get different labels - for example, if the model's output is [0.34, 0.66], one threshold could be if the threshold for class 1 is set to 50% then the output of the model is labeled 1. If we set the threshold to, say 70% then the output changes to 0. So playing around with the threshold gives you different outcomes for the ROC curve.

@my_master55 2 жыл бұрын

@@ml_dl_explained oh, okay, so ROC and AUC are mostly used for the binary classification?

@ml_dl_explained 2 жыл бұрын

Yes. exactly