def sigmoid(x): x = np.clip(x, -500, 500) return (1/(1+np.exp(-x))) To avoid overflow runtime error as the return statement can reach large values

went through when I first started video editing, now it's taking a whole new switch and learning soft will only boost my courage for the

Best concise video on logistic regression I have seen so far

@AssemblyAI

Жыл бұрын

That's great to hear, thanks Josiah!

Thanks for sharing this, I am doing something similar in JavaScript. The part about calculating the gradients for backpropagation is very helpful!

Great work from @AssemblyAI 👍✨thank you from India.

This was a great video, will there be one in the future that covers how to do this for multiple classes?

Great work! Thank you :)

how is the derivative of loss function w.r.t weights same for cross entropy loss and MSE loss ?

Studying CSE in GUB from Bangladesh , Love the way you teach the explanation & everything ; )

Love it. Keep up the good work

Very good! Thanks for your videos!

need more algorithms , you are the best

@AssemblyAI

Жыл бұрын

Thank you!

Excellent video. Thanks.

Wow , what a great video, very helpful

@AssemblyAI

Жыл бұрын

Glad it was helpful!

Great video, but my only dubt comes when J'() is calculated as the derivate of MSE and not as the derivate of the Cross Entropy, which is the loss function that we are using

@rbk5812

Жыл бұрын

Found the answer yet? Please let us know if you do!

@GeorgeZoto

Жыл бұрын

That's what I noticed too.

@zahraaskarzadeh5618

Жыл бұрын

Derivation of Cross Entropy looks like derivation of MSE but y^ is calculated differently.

@upranayak

11 ай бұрын

log loss

@HamidNourashraf

7 ай бұрын

We should start with Maximum Likelihood. Take the log. Then take derivative with respect to B (or W). Not sure how she took derivative. I guess she used MSE for classification problem instead of using Binary Cross Entropy. 🤔. Log L(B) = Sigma_{i=1}{N}((y_i*B*X_i - log(1 + exp(B*X_)).

AWESOME EXPLANATION THANKS A LOT !!

@AssemblyAI

Жыл бұрын

You're very welcome!

Amazing video, I'm liking so much this free course, I'm learning a lot, thanks! 😁💯😊🤗

@AssemblyAI

Жыл бұрын

Happy to hear that!

I have a question, why can't you include accuracy function in class module?

superb video! I am saying that because coding from scratch is important for me.

Awesome work, also, great English !

love your code 👍

@AssemblyAI

Жыл бұрын

Thank you!

We should maximize likelihood or minimize minus likelihood, I think the cost function is missing a minus , Am i right ?

When you write the dw and db, shouldn't be (2 / n_samples)? There is the 2 in the derivative that you can take outside of the summation

@hitstar_official

5 ай бұрын

That's what I am thinking from previous video

Hello @AssemblyAI, where can I find the slides?

What if you have categorical data? Like if different scale

wow you are an amazing teacher thanks alot god l love youtube !!!!!

Thanks 🙏 ❤

I have seen other videos where people use a ReLU function instead of sigmoid. Would this logistic regression algorithm be an appropriate place to use ReLU instead of Sigmoid? If not, why not?

@mertsukrupehlivan

10 ай бұрын

Logistic regression typically uses the sigmoid activation function because it provides a probability interpretation and is mathematically suited for binary classification. ReLU is more commonly used in deep neural networks with hidden layers, not in logistic regression.

why you have not used summation for dw for calculating error

with the partial derivatives, where did the multiple 2 go?

Hi, does an increase in sample size increase the prediction accuracy?

@robzeng8691

Жыл бұрын

Look for statquest's Logistic regression playlist.

Please I want to build a personality assessment through cv analysis using this model, could you please help me?

Amazing

Super 👏👏

Does this use regularization?

You are superb

Amazing video! Can you add the plot of it?

Is there a way to visualize this?

how can find presentation file.

Can we make this work for multiclass classification?

@nehamarne356

Жыл бұрын

yes you can use logistic regression for problems like digit recognition as well

The same problem of missing the 2 multiplication in calculating dw, db in the .fit() method: dw = (1/n_samples) * np.dot(X.T, (y_pred-y)) * 2 db = (1/n_samples) * np.sum(y_pred-y) * 2 It does not affect too much, but we follow the slide to not be confusing

@LouisDuran

10 ай бұрын

I noticed this as well. But adding in those *2 reduced the accuracy of my predictor from 92.98% to 88.59%

@armaanzshaikh1958

Ай бұрын

But in bias why we are not using mean coz the formula says summation then it has 1/N too ?

7:51 Why you didn't multiplied by 2 the derivatives?

@stanvanillo9831

4 ай бұрын

Technically you would have to but in the end it does not make a difference, you only effectively half your learning rate if you don't multiply by two.

@armaanzshaikh1958

Ай бұрын

Same question arises for me too why it wasnt multiplied by 2 and for bias it took sum instead of mean ?

Can it be used to predict house prices?

@AssemblyAI

Жыл бұрын

Why not!

My accuracy came to be around 40, even after tweaking with n_iters, lr couple of times, is that okay ?

@prateekcaire4193

4 ай бұрын

same

@prateekcaire4193

4 ай бұрын

I think we made the same stupid mistake where we did not loop through for n_iters. for _ in range(self.n_iters):. silly me

wish one day i will be in this level of coding

Relevant feature selection not shown

Like it most

@AssemblyAI

Жыл бұрын

Awesome!

NICEEE ♥💙💙💚💚

Why doesn't this algorithm work on the Diabetes dataset? I'm getting an accuracy of 0

I didn't know exactly why you imported Matplot Library

تو عالی هستی❤

7:09 The gradient should not have the coefficient 2 . 7:43 linear_pred there should be a minus sign before np.dot.

@noname-anonymous-v7c

5 ай бұрын

The above mentioned issue does not have much effect on the predicting result though.

make for multiclass also

A M A Z I N G

I haven't used enough Python yet to accept the soul-crushing inevitability that there's going to be "self." everywhere. I guess you could call it "self." hatred. Maybe ligatures could come to the rescue, replacing every instance with a small symbol. While we're at it, put in ligatures for double underscores and "numpy." (or in the case of this video, "np."). Yes, it's an aesthetic rant that is ultimately not a big deal, but gradient descent is a beautifully simple concept. The presenter does a great job of matching that simplicity with clean, easy to follow code. Maybe it's not such a bad thing to be irritated at the parts of her code which are inelegant only because the language doesn't give her better options.

Where is the backward propagation? In conclusion logistic regression is also a neural network

In predict(), is it X.T or just X for the linear_pred dot product?

But why are we using numpy here it's supposed to be from scratch

♥♥♥♥♥

-1/N

What is this witchcraft? I thought ML was supposed to be too hard to know wth is going on!

Amazing video but you're going over too fast

@AssemblyAI

Жыл бұрын

Thanks for the feedback Lucian!