Logistic regression | Likelihood and deviance

Показать описание

In this video, we will learn how to calculate the likelihood and the deviance based on some simple example data.
1. Example data
2. Binomial and bernoulli distributions (02:32)
3. Likelihood (05:48)
4. Maximum likelihood (09:38)
5. LL of the null and the saturated model (11:04)
6. The deviance (14:02)
7. McFadden's pseudo R2 (15:30)
For more videos, go to

Рекомендации по теме

Комментарии

You have done a fantastic job explaining the details! Thank you! Your video is one of the top videos on YouTube on the topic.

romaniup

If someone just using equation and abstract dataset, I will be completely lost, so I really like the way you explained the concept, by using detailed and realistic dataset, with such plenty and friendly arrows to indicate the positions we should focus, it significantly help me to get fully understanding of it.

sasakevin

Man, that's a great explanation. I'm preparing for a test at the uni regarding logostic regression and you saved me a lot of time I would need to spend on reading a statistics book.

mateusztaflinski

Very very good complement material for an analysis course I am taking! Thank you soooo much!

rosemo

Very good and clear explanation, 1:28 The P(Cancer) nominator should be e(BX)

kahmengsoh

Huge thanks
Can we say the pseudo-r-square is the same as deviance ratio that is reported in some statistical packages after logit models

VivekGupta-shlj

Hi Sir,
Please provide videos gradient descent and ascent in case of possible.
Which can help us more.

ratnakarbachu

Hi, thank you so much for the great videos. Quick question about the interpretation of residual deviance. I understand the lower the value, the better. Is it because the LL (proposed model) inside the residual deviance formula follows the same principle? Thanks in advance

datascience

thanks for the great video! at 11:32 why b0 is 0 at null model? could you please illustrate? many thanks!

Tyokok

Great job! So, the null model of logistic regression is essentially always that both outcomes have an equal probability of happening? The null model is it's all due to chance?

brazilfootball

Uhmm sir, my proff. Stole your slides and he is teaching us of them is that legal?

shwamzad

I can't understand how obtain -5.754 and 2.747.

giuseppepersico

I still don't know how b0=-5.747 and b1=2.747. I used cancer=1 and healthy=1 on the dataset and i have b0=1.5 and b1=1.25

DUYTRẦNVIẾT-nt

Logistic regression | Likelihood and deviance

Logistic Regression Details Pt 2: Maximum Likelihood

Logistic regression | Likelihood and deviance

Logistic Regression with Maximum Likelihood

Logistic Regression in 3 Minutes

Maximum Likelihood, clearly explained!!!

Logistic Regression [Simply explained]

Logistic regression | Likelihood ratio test and AIC

StatQuest: Logistic Regression

RPRIT training logistic regression

Logistic Regression - THE MATH YOU SHOULD KNOW!

4. Fitting Logistic Regression using Maximum Likelihood Estimate (MLE).

Odds and Log(Odds), Clearly Explained!!!

Linear Regression vs Logistic Regression - What's The Difference?

Maximum Likelihood Estimate for Logistic Regression

A Modern Maximum-Likelihood Theory for High-Dimensional Logistic Regression

Explaining logistic regression

Logistic Regression And Maximum Likelihood Principle

Logistic Regression: Logistic Vs. Linear Regression, Log Odds & Maximum Likelihood

How to Derive the Maximum Likelihood Estimators for Logistic Regression

Logistic regression log odds and Maximum likelihood

Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy

Logistic Regression Details Pt 3: R-squared and p-value

Log odds interpretation of logistic regression

Unlocking Logistic Regression: Understanding the Log-Likelihood Loss Function