What are some alternatives?
When comparing miceforest and scikit-learn you can also consider the following projects:
Keras
– Deep Learning for humans
Prophet
– Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Surprise
– A Python scikit for building and analyzing recommender systems
tensorflow
– An Open Source Machine Learning Framework for Everyone
gensim
– Topic Modelling for Humans
H2O
– H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
PyBrain
seqeval
– A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc…)
MLflow
– Open source platform for the machine learning lifecycle
xgboost
– Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
TFLearn
– Deep learning library featuring a higher-level API for TensorFlow.
Pytorch
– Tensors and Dynamic neural networks in Python with strong GPU acceleration
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