# Tag Info

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One option would be to feed an array of both variables to the stratify parameter which accepts multidimensional arrays too. Here's the description from the scikit documentation: stratify array-like, default=None If not None, data is split in a stratified fashion, using this as the class labels. Here is an example: import numpy as np import pandas as pd ...

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Missing at random (MAR) means the NA frequency of the variable is never depended on the value of the variable itself. Therefore in your example the data would be Missing not at random (MNAR)! Why is this distinction important? Because when the data is MNAR we have to identify the relationship between missingnes and the value however if the data was truly MAR ...

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The simple option is to design your features so that they represent the distribution of the values: every feature $f_i$ represents a bin and its value for a particular instance is the frequency of the corresponding range for this instance. Example: let's consider 10 bins between 0 and 1, i.e. $f_1=[0,0.1), f_2=[0.1,0.2),..., f_{10}=[0.9,1]$: \$x_1=[0.2, 0.25,...

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You can try the below code to merge two file: import pandas as pd df1 = pd.read_csv(‘first.csv’) df2 = pd.read_csv(‘second.csv’) df = df1.merge(df2, on=‘Column1’)

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You can use google drive to save these and import the drive on your code.

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One definition for "modality of data" is how many different types of data are included in the dataset. For example: Images along with tags and text. Different modalities usually have very different statistical properties, which can make the dataset more complex to work with.

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The search term you are looking for is limit cycle. You can find some examples (legged-locomotion, digital control systems, etc.) here: https://www.sciencedirect.com/topics/engineering/limit-cycle.

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So your question is asking how to go about making a model which predicts whether the YouTube comment is paedophilic given the text provided in the YouTube comment itself. Before I provide an answer, it is worth considering the ethical applications of this model and what this will be used for because models do implicitly cause bias in all stages of model ...

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May try these two Kaggle datasets - camnugent/california-housing-prices kumarajarshi/life-expectancy-who Try your question here - https://opendata.stackexchange.com/

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I'm not especially familiar with this but from the example provided we can deduce that: An hypothesis is a partial assignment of values to the features. That is, by "applying the hypothesis" we obtain a subset of instances for which the features satisfy the hypothesis. An hypothesis is consistent with the data if the target variable (called "...

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