Python vs R for machine learning - Data Science Stack Exchange
Jun 12, 2014 · I'm just starting to develop a machine learning application for academic purposes. I'm currently using R and training myself in it. However, in a lot of places, I have seen people using …
Best practices to store Python machine learning models
What are the best practices to save, store, and share machine learning models? In Python, we generally store the binary representation of the model, using pickle or joblib.
python - What do x and y mean when working with data in the …
Dec 14, 2021 · What do x and y mean when working with data in the machine learning domain? Ask Question Asked 3 years, 11 months ago Modified 2 years, 6 months ago
python - How do I handle string feature while performing model ...
Feb 15, 2019 · Polynomial Coding Sum Coding Target Encoder Weight of Evidence Wrappers Quantile Encoder Summary Encoder A Kaggle notebook - 11 Categorical Encoders and Benchmark on using …
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machine learning - Manual Python Implementation of Stacking Model ...
Jan 2, 2025 · I tried to build a Python class, CustomStackingClassifier(), to implement the Stacking method in ensemble machine learning. In this implementation, the output of the base classifiers is set …
python - Incremental Learning with sklearn: warm_start, partial_fit ...
Answering my own question after some investigation: warm_start=True and calling .fit() sequentially should not be used for incremental learning on new datasets with potential concept drift. It simply …
machine learning - How to train ML model with multiple variables ...
Oct 2, 2018 · Start asking to get answers Find the answer to your question by asking. Ask question machine-learning python scikit-learn
machine learning - Best python library for neural networks - Data ...
May 29, 2017 · I'm using Neural Networks to solve different Machine learning problems. I'm using Python and pybrain but this library is almost discontinued. Are there other good alternatives in Python?
machine learning - How can I check the correlation between features …
Now using some machine learning on this data is not likely to work. There just is not sufficient data to extract some relevant information between your large number of features and the loan amount.