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Generally, I'd pick a very simple, transparent/explainable model and use the results in a semi-automated way. That is, do not just derive a prediction but rather insights. You could, for example, use a (or multiple) decision tree(s) which you pre or post prune. The result could be a tree with, let's say, just 1-3 features to find simple rules like "if a ...


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The main problem with very little data is that it's almost impossible to know how representative the sample is. Some people would even say that less 20-30 data points cannot be representative of anything. Every single data point can have a huge impact on any model, so any prediction has a huge margin of error. If one is going to train a model from a tiny ...


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The best way of solve this is using pipelines as follows: Working example: import pandas as pd import numpy as np from sklearn.pipeline import Pipeline from sklearn.compose import make_column_selector, make_column_transformer from sklearn.linear_model import LogisticRegression from sklearn.impute import SimpleImputer, KNNImputer from sklearn.preprocessing ...


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There is no obstacle to doing this. For example you can create data by make_classification, and compare different algorithms by building model on it. You can also pass a random_state value to obtain same data each time you call the function. Both SVM, and Decision Trees can work with continuous data.


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Assuming that these are pytorch tensors, you can convert them to numpy arrays using the .numpy() method. Depending on whether your tensors are stored on the GPU or still attached to the graph you might have to add .cpu() and .detach().


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You first create inverted indexes or postings list. Then, using the term frequency and document frequency you calculate tf idf with the formula $tf* log({N \over df})$. For more details check this blog.


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It depends on the problem you are working on. If number of categorical variables is very large, it is better to use label encoding. But the label encoding should be meaningful i.e. the categories which are close to each other should get similar labels. Let's say you are creating a model where you have a feature Month. But there is a periodicity in your ...


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Ignoring for a moment the variance of the first feature, a straightforward approach is to perform a linear combination of the features $x_1$, $x_2$ and $x_3$, with each of the coefficients being a hyper-parameter you set to indicate the relative importance of that feature (perhaps normalizing the features before hand). This will be your molecule's utility ...


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PCA removes the connection with the original features,so the interpretation of the visualisations in the principle component space is therefore not very meaningful. E.g. cluster A has higher values of PC1, where cluster B has higher values of PC2. If you can clearly see that PC1 is only representative of Feature X, then fine, but this isn't often the case. ...


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