Explore tweets tagged as #sklearn
第27回「近ごろの機械学習ライブラリ（4）PyCaret」 小澤昌樹氏のデータ分析コラムを公開しました。興味がある方は是非ご一読ください。 https://t.co/jMR6qqis0Q -------------- こんにちは、小澤です。 前回は、auto-sklearn や H2O AutoML などの AutoML（自動機械学習）ツール
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Merging @DSPyOSS and @scikit_learn, we created DSPyMator! DSPyMator is basically a *magic* sklearn estimator. The magic, of course, is simply the magic of DSPy repackaged into the familiar sklearn-style fit/transform/predict API design to plug into traditional ML workflows.
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How old were you when you got to know that sklearn predict_proba() doesn’t actually return guaranteed probability? Simple explanation👇🏻: - Most classification models always has to pick a class (ex, spam vs ham). - It gives you a probability number between 0 and 1.
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1. The Python Machine Learning and Deep Learning Libraries: - mxnet - gluon - sklearn - xgboost
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After, I trained my first neural network using scikit-learn (sklearn) on the California_housing dataset already (pre processed) , got the data, split into train, validation and test dataset, then train the dataset with (MLPRegressor (SKLEARN NN) ) and the RMSE value was 0.5053
I learnt about the back propagation algorithm, gradient descent and from the infographics in fig 4, the backprop was not represented well @NanoBanana, do better (free to debate), what mini batches and epochs are, neural net is cool and math too
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🚀 Scikit-learn is slow and clunky? There's a better way! 🚀 Did you know you can run Random Forest 🌳 in LightGBM? ⚡ If you've been using `sklearn` for Random Forest and finding it sluggish on large datasets, it's time to switch things up!
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אמ;לק: מחפש עבודה, רטווטו! היי פיד, אני סטודנט להנדסת נתונים ומידע בטכניון בשנה ג' על ממוצע 89 ואני מחפש משרת סטודנט בתחום הדאטה. השלמתי קורסים בML, סטטיסטיקה, מבני נתונים, אלגו' ועוד, ויש לי בידע בפייתון (Numpy,sklearn,pandas), SQL ושפות תכנות נוספות. אשמח לחיבורים וריטווטים 🙏
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Finally Completed my first data science project I know the stats are not pretty good but will work on it in future learnt alot like reading graphs, random forest and sklearn libs, vs code >>>> jupyter lab
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第26回「近ごろの機械学習ライブラリ（3）Auto-sklearn、H2O AutoML」 小澤昌樹氏のデータ分析コラムを公開しました。興味がある方は是非ご一読ください。 https://t.co/Yo7i6eXD4z -------- こんにちは、小澤です。 前回は、PyTorch（パイトーチ）
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free "Python" handbook for Machine Learning & data science. It includes essential Python libraries: Numpy, ipython, Pandas, Matplotlib, Sklearn... https://t.co/gmX5J3jJhv
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🚀 Own https://t.co/dPhAu1jAzf , a premium domain for AI & machine learning innovators! Sklearn is a leading Python ML library simplifying data science. Perfect for domain investors targeting AI. #DomainSales #AI #Sklearn #MachineLearning #PremiumDomains
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Study Log 226: Multiple Linear Regression. Written the whole thing in Python (compute for gradient, cost, gradient descent, rmse, one-hot encoding). The result is "acceptable" since the RMSE of the gradient descent is not far off with sklearn's linear regression. Tomorrow, I'll
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Scikit-learn has long-documented issues. See this Y Combinator thread on the pattern of ignorance and design flaws. "Unfortunately scikit-learn is a mess without an alternative. There is so much wrong with the API design of sklearn (how can one think predict_proba is a good
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📘 Day 36: AI/ML Journey 🔹 Wrapped up Model Selection Process Train/validation/test split Avoid MCP (lucky models) with a final test set Steps → Split → Train → Evaluate → Select → Test 🔹 Next → Environment Setup ✅ Python 3.11, NumPy, Pandas, Sklearn, Matplotlib
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✨ Day 50 of #100DaysOfCode Started learning Machine Learning with Python today! 📊 Cleaned missing data using SimpleImputer from sklearn. Excited for what's ahead in this journey! 🚀 How did your ML journey begin? #buildinpublic #Connect #MachineLearning #Python #DataScience
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Check out this great book >> 50 Days of Data Analysis with #Python — The Ultimate Challenges Book for Beginners, with Hands-on Challenges using Pandas, NumPy, Matplotlib, Sklearn and Seaborn: https://t.co/bvoyFrk3Jf by @RealBenjizo ————— #DataScience #DataScientist
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I trained my first predictive model, using sklearn linear regression. It is predicting the price for Boston homes using some features like distance from employment area, number of rooms, status of people, close to the river, and many more. This is just the first of many Gm 😊
The goal Lockin on Ai engineering
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