Comparing machine learning models in scikit-learn

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We've learned how to train different machine learning models and make predictions, but how do we actually choose which model is "best"? We'll cover the train/test split process for model evaluation, which allows you to avoid "overfitting" by estimating how well a model is likely to perform on new data. We'll use that same process to locate optimal tuning parameters for a KNN model, and then we'll re-train our model so that it's ready to make real predictions.

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Really simple to understand. Doesn't make it seem like "its a library thing, library does it for ya". Thank you for doing this

rajatpai

That's some killer delivery, you didn't waste a word! Great tutorial!

siddharthkotwal

This is by far the best Sci-kit Learn tutorial on Youtube. I can say this because I have seen almost every tutorial and this covers everything starting from scratch.I knew how all the algorithms work but what I needed was how do I implement those algorithms from loading the data set to all terminologies to checking the accuracy and what not and this series has everything I was looking for, thank you so much for this.Really appreciate it.

suhailchougle

"models that overfit have learned the noise in the data rather than the signal" - yes, well said!

WanderingJoy

I like the pace of these videos. You speak really slow and clear which helps your viewer to digest the information on the fly. Loving your work!

LordBadenRulez

Dear Kevin. To me your videos are a reference, as those of Mr Andrew Ng. Very good job! Thank you very much from Spain :)

pabloalonso

Your way of delivery is exceptional. I have never seen somebody teaching so well like you. I made me interested in ML Thanks bro...God bless U

priyanshugupta

Your teaching style is outstanding. As someone who has used R in the past, I really appreciate the clarity of your explanations and demonstrations.

frankacito

The last two videos are the best ones I’ve seen someone explain scikit learn’s predictions. Every other video jumps straight to the full analysis but in reality, you can predict in as little as 4 lines of code. Great job!

beansgoya

This is such a gem for beginners .Thank you very much Kevin

uppubhai

This video series sets such a high standards for Content, Context and Delivery of Machine Learning training ! Its a winner for all those who are starting to learn Machine Learning !! Thank you so much for your efforts Kevin !!!

pranavjoshi

Uno de los mejores manuales sobre "Machine learning" que he visto. Gracias por ofrecernos la oportunidad de aprender. Además, tu pronunciación es perfecta para hispanohablantes

miguelgutierrez

I thank God I landed on your videos. I see things clearer than ever. You are a gifted tutor. God bless you sir.

FULLCOUNSEL

A student from CN jumping across the Great Wall learned this excellent class. Thx.

daokou

loving this series man just started out with ML and DS understanding everything

kushsheth

Wow I must say your teaching style is amazing. Very organized, thorough and easy to follow. Thanks for your time, and keep making great videos! I wish more professors were like you at my school.

juancastillo

Man, he just makes it so easy to learn.

Wish we had half as good teachers as him in school.

TheGautamj

Most notable take aways from the video:

- "Plotting testing accuracy vs model complexity is a very useful way to tune any parameters that relate to model complexity."

- "Once you have chosen a model and it's optimal parameters and are ready to make predictions on out of sample data, it's important to re train your model on all of the available training data."

- Repeating the train/test split process multiple times in a systematic way using k fold cross_validation

fubarsid

My confident level is super high to learn Machine Learning after seeing this video. Your every word is very clear and correct. Thank you very much.

kamruzzamantanim

I was looking for ML tutorials and can say that your videos are simply the best.Thanks a lot

galustbayburcyan

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