I'm working on a project that involves analyzing a new dataset, and I'm at the stage of selecting the most appropriate machine learning algorithm. The dataset consists of both numerical and categorical features, and my primary goal is to achieve high predictive accuracy.
What factors should I consider when choosing the right machine learning algorithm for my dataset? Are there any specific guidelines or methodologies that can help in making this decision? Additionally, how can I effectively balance between model complexity and interpretability?
Any insights, recommendations, or resources that can guide me through this selection process would be greatly appreciated. Thank you!
You can start with basic Regression and Tree models to set a baseline, as they are fast and interpretable. You can move to more complex models like Random Forests, Gradient Boosting Machines, and Neural Networks. There are always trade-offs between interpretability and accuracy, and there is no one algorithm that fits perfectly for a dataset, it involves experimentation with multiple algorithms and evaluating their performance.
In addition to the points mentioned by Dileesh Chandra Bikkasani, to find the most appropriate machine learning model, you can utilize frameworks such as scikit-learn in Python and tidymodels or caret in R. These frameworks provide a wide range of tools and functions to streamline the process of model selection, training, and evaluation, making it easier to compare different algorithms and choose the best one for your specific dataset. You can find detailed information about these frameworks at the following links:
- scikit-learn: [https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)
- caret: [https://topepo.github.io/caret/](https://topepo.github.io/caret/)
- tidymodels: [https://www.tidymodels.org/](https://www.tidymodels.org/)
In addition to all above, your data size will determine this. Check this link for more info. https://blogs.sas.com/content/subconsciousmusings/2020/12/09/machine-learning-algorithm-use/
I suggest trying a set of different classifiers and comparing their performance. You can also develop a system that considers different classifiers in the output.
Maybe this paper can help you.
Article Hybrid approaches to optimization and machine learning metho...
Observe the datasets.
Convert those categorical data into numercial.
Check multicolinearity
Lesser multicolinearity gives you better regression model.
Observe those datasets again, put regression model based on your target variable
An easy way to do this would be using the Lazypredict library. Check that out. It iterates over your code and gives you the performance metrics of all algorithms making it easy for you to choose.
In addition, you can check the features that most relate to your work, also balancing of the data set
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