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You're right I'm not sure if I followed exactly what you said, however for your overall question of can a Machine Learning model be used as a form of compression - yes!
A good example is an Autoencoder, which is a neural network that 'breaks down' the input into basic elements and then reconstructs the input using only the elements required. This is ...
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If the data for each volunteer have the same format, then you can proceed in following manner.
Step I
Combine the 7 CSVs of each volunteer into a single CSV. Merge these CSVs column-wise (and do not append rows). I assume that your sensor data columns are features (X) and annotation column/columns are target (y). So now you have 10 CSVs, one for each ...
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A baseline model is a naïve way of predicting. People who do this kind of work sometimes have a company pay them to do so. Justify your salary to the company by achieving better performance than they could get without much work.
EXAMPLE: If you want to predict the expected stock price tomorrow, naïvely guess today's price. You don't need a fancy data science ...
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Generally speaking, the "normal" shape of a learning curve (defined as a "plot of error vs training set size is known as a learning curve" (1)) is to observe an initially very low training error indicating that the model almost perfectly learns the small amount of training data while the test error will be high. When the amount of ...
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I think there is a good bit of confusion in how you define Machine Learning:
There are different types of learning: if the model is trained with data annotated with the "correct" answers, then it's supervised learning.
There are many different "families" (methods) for supervised ML. Your description focuses on Neural Networks, but there ...
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Hello and welcome to the site! You can use list(range(1,100)) to get what you want. However, questions like this (how to achieve something in Python) are more suitable for stackoverflow. The community here focuses on data science related questions, as the name suggests.
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I've tried to create a function as suggested but it doesn't work for my code. However, as suggested from an example on Kaggle, I found the below solution:
import shap
#load JS vis in the notebook
shap.initjs()
#set the tree explainer as the model of the pipeline
explainer = shap.TreeExplainer(pipeline['classifier'])
#apply the preprocessing to x_test
...
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Random Forest is a tree based ensemble algorithm which uses bagging to improve the performance of the algorithm. There are few improved/modified Random Forest algorithms which includes weighted quadratic random forest and weighted class random forest, to name a few. These algorithms are not mainstream algorithms and can be used for very specific purposes - ...
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