37 thoughts on “Comparing machine learning models in scikit-learn”

  1. Note: This video was recorded using Python 2.7 and scikit-learn 0.16. Recently, I updated the code to use Python 3.6 and scikit-learn 0.19.1. You can download the updated code here: https://github.com/justmarkham/scikit-learn-videos

  2. Thank you very much again! I look forward to learning mode on the various libraries and models for machine -learning, with great examples as usual. Greetings!

  3. You gotta superb way of explaining things in a very simple manner. Request you to share one detail video on data visualization in pandas and one complex data science project.

  4. I am new in machine learning and now I am facing an issue. I have 7 projects I would like to predict whether a pull request would be rejected or not (Yes or No). And I would like to build a prediction model by using data from 6 projects as source project and predict the rejection of the pull request in the seventh project as a target project. Can you please tell me how can I structure my algorithm in Scikit-learn? Hope that my question is clear.
    Thanks

  5. I ADORE YOU πŸ’•πŸ’•πŸ’• you teach difficult concepts really well. Thank you and i hope you keep posting.

  6. Hi Kevin,
    Do you have any blog which you continuously update to stay in touch with your latest info on machine learning/Data science articles ?

  7. Hello,

    I don't understand why eventually we fit the whole dataset into the model after we get the best k value after we use training and testing dataset?

    Jason

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

  9. OMG finally found a ML tutor who is awesome…. i cant skip any seconds in your videos, even each words are informative

  10. Hello Kevin,

    Is there a particular reason to choose the middle of the range of optimal K (22:10), or would there be cases where we could want to take the lower/high (6~17) value of K in the range of optimal accuracy, after plotting K and model accuracy? Thank you!

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

  12. Great series of courses on Pandas and Scikit Learn! I’ve been enjoying every video I watched on this channel. Thanks so much!

    On machine learning using Scikit Learn, I’m wondering if you could share a lesson on Random Forest and related concepts. Thanks again.

    Terry

  13. how to deal with 3d medical images dats sets and suppose I have a 3d image then how i can test that image to get the categorical
    result

  14. Thanks so much for making such amazing material, i am more motivated that i can further work in data science. kudos bro

  15. Thank you very much for your valuable time explaining machine learning. I will always think how they predict the future value. Now I know.

  16. There are so many parameters within each model to tune. So how to decide
    1. On which and On how many different parameters to tune for our problem statement and
    2. On how many different models to try before finalising the model.

  17. Dude, thank you very much for this set of videos. I'm from Brazil and I'm really enjoying youe course.

  18. Great videos Sir. But i have one question i.e. how come the accuracy is not 100% since we are predicting on the same features X that we fit it with :
    logreg.fit(X,y)
    y_pred = logreg.predict(X)
    from sklearn import metrics
    print(metrics.accuracy_score(y, y_pred))
    0.96

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