Online machine learning in python with river
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online machine learning is a technique where a model is trained continuously as new data becomes available, without retraining the entire model from scratch. this is particularly useful when dealing with streaming data or when the data is too large to fit in memory all at once.
one popular python library for online machine learning is river. river is a lightweight and fast library that provides a wide range of online learning algorithms and tools for machine learning tasks.
here's a step-by-step tutorial on how to get started with online machine learning using river in python:
step 1: install river
you can install river using pip:
step 2: import the necessary modules from river
step 3: choose a dataset
for this tutorial, let's use the "higgs" dataset from river:
step 4: initialize an online learning model
let's use a linear regression model from river:
step 5: train the model and evaluate its performance
in this code snippet, we iterate over the dataset one sample at a time, make predictions using the model, update the model with the new sample, and evaluate the model's performance using the r2 score.
step 6: make predictions with the trained model
once the model is trained, you can make predictions on new data using the `predict_one` method:
this tutorial covers the basics of online machine learning using river in python. river provides a wide range of online learning algorithms and tools that can be used for various machine learning tasks. feel free to explore more algorithms and functionalities offered by river to enhance your online machine learning projects.
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