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The correlation does not effect your model using decision trees in a classification problem. In the theory of decision tree models, you don`t need correlation or check of multicollinearity. Because the split in decision trees is made of entropy/information gain. The correlation does only check linear dependencies. The same is, when the dataset is highly ...


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It's a general question, so there are more then a few things you can do. Although, what stopping you to train a basic clssifier and investigate the results? Some ideas: Use Predictive Power Score to keep on investigate your data Check for non-linear correlation between the features Investigation the features importance Use dimension reduction Check for ...


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not sure if I understand correctly but it might be done with a command like this df['category'] = np.where(df['category']!='0', '1', '0')


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I believe boxplot or violin plot is a good idea and you could overlay datapoints with a bit of jitter to the former. See below an example in seaborn taken from a relevant question: import seaborn as sns import matplotlib.pyplot as plt tips = sns.load_dataset("tips") sns.violinplot(x="day", y="total_bill", data=tips, color=&...


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I think it would be better to use a standard scaler that removes the mean and divides by the standard deviation. See here for more info and an implementation using sklearn. Why? At least you should be aware that dividing by the maximum could hide smaller effects. In the case you have an outlier that has a very high value, you would loose the small changes in ...


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The answer by edmund is quite cool because it shows the algorithms and methodology that I need, but unfortunately his answer was about the wolfram language that I don't know and I don't really want to learn a new language right now. But some digging and googling has turned up some good alternatives. Specifically Open3D and sklearn became my tools of choice. ...


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You should Go along with Tensorboard, it looks more professional than the rest and it is easier to understand the model. May I ask your exact purpose for the visualization? You could check others material with the similar purpose and go along with the general choice! Many would go for draw_convnet or PlotNeuralNet if the final use-case was to publish a blog. ...


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There are many possible answers to this question. Data science applied to mechatronics and cybernetics can involve physical information, which I believe is what you are describing. The DS side of mechatronics often involves IoT and engineering cybernetics principles. More specific sub-fields include design optimization and control theory. Edit to add a few ...


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