dtreeviz
feature-engineering-tutorials
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| dtreeviz | feature-engineering-tutorials | |
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| 3 | 1 | |
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dtreeviz
- Dtreeviz: Decision Tree Visualization
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Pybaobab – Python implementation of visualization technique for decision trees
Not really. If we are doing data science for many companies and explainability is an aspect we will likely go for decision trees if the lift for advanced models is minor anyways. We use this (not quite as pretty for visualization but extremely useful to get a grasp if the tree model): https://github.com/parrt/dtreeviz
- How to Visualize Decision Trees
feature-engineering-tutorials
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How to balance multiple time series data?
I’ve actually solved a similar problem several times in a variety of settings. I’ve had success with boosted trees and feature engineering on the sensor readings over time. I treat each reading as an observation and set the target to be the value I want to forecast (e.g. one hour ahead, the sum over the next day, the value at the same time the next day). There was a recent paper that compared boosted trees to deep learning techniques and found the boosted trees performed really well. Next, I perform feature engineering to aggregate the data up to the current time. These features will include the current value, lagged values over multiple observations for that sensor, more complicated features from moving statistics over different time scales, etc. I actually wrote a blog about creating these features using the open-source package RasgoQL and have similar types of features shared in the open-source repository here. I have also had success creating these sorts of historical features using the tsfresh package. Finally, when evaluating the forecast, use a time based split so earlier data is used to train the model and later data to evaluate the model.
What are some alternatives?
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
jupyter-notebook-chatcompletion - Jupyter Notebook ChatCompletion is VSCode extension that brings the power of OpenAI's ChatCompletion API to your Jupyter Notebooks!
eli5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions
intro-to-python - [READ-ONLY MIRROR] An intro to Python & programming for wanna-be data scientists
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
ydata-quality - Data Quality assessment with one line of code
psych-verbs - Research experiment design and classification of Romanian emotion verbs
gastrodon - Visualize RDF data in Jupyter with Pandas
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.
PRML - PRML algorithms implemented in Python
Employees-Burnout-Analysis-and-Prediction - The "Employees Burnout Analysis and Prediction" GitHub repository is a project focused on analyzing and predicting employee burnout in an organization.
desbordante-core - Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.