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For this type of issue, I typically add the reciprocal of the log base. For data that's being log10-scaled, this results in adding 0.1 to all values. For data that's being log2-scaled, this results in adding 0.5 to all values. This has the nice property of mapping all of your 0 values to -1 in the log scale, regardless of what log base you use. If your data ...
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Your suggestion is a valid one, encoding variables with a known outcome once the scaling is applied. Log(1) will become zero, so just keep that in mind for your next stage. You can use clip or replace for this:
df.clip(1, df.max())
or try replacing with a NaN
df.replace(0, np.nan)
Alternatively you could do one of the following:
Drop the zero value rows e....
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The normalisation you do does not re-scale to $[0,1]$ range! It normalises to have mean $0$ and std $1$ instead.
To scale the tensor to be in $[0,1]$ range you should subtract min value and divide by absolute max-min value.
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Well, there are a few ways to do the job. Here are some I thought of:
Scatterplots with noise:
Normally, if you try to use a scatter plot to plot two categorical features, you would just get a few points, each one containing a lot of instances from the data. So, to get a sense of how many there really are in each point, we can add some random noise to each ...
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When imputing data, one is looking not to modify the true distribution of your data. So a way to test how good your imputation was is to make a test to contrast the true distribution of every feature that has been imputed vs the true (via KS test for example) distribution of the feature (prior imputing) if you can sate with a level. of confidence that your ...
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have you already tried using only very little of the -ve cases? So for example to train your model on 900 points total, 600/300? Then stratified sampling should still work fine. Then I'd evaluate your model based on it's ability to predict -ve cases and just monitor the performance it get's on the (in your case) gigantic test dataset that the model hasn't ...
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Following on Thomas on the relation between the Bray Curtis distance and the F1 score and the calculation of the first and second-order derivatives. If one defines the Bray Curtis distance between vector X and Vector Y as: $\sum |X_i-Y_i| \over {\sum (X_i+Y_i)}$, than the first derivative to $x$ is $d \over (dx)$ $|x - y| \over {(x + y)}$ = $2y(x - y) \over{\...
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The error comes from attempting to fit a classifier (logistic regression) on a regression problem. If you are trying to predict prices (continuous outcome), you should use linear regression.
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I'm using the code below to take images using OV2311 and RPI Zero w. However, I don't want my previous image to be overwritten.
I therefore request for help from experts here.
import arducam_mipicamera as arducam
import v4l2 #sudo pip install v4l2
import time
def set_controls(camera):
try:
print("Reset the focus...")
camera.reset_control(v4l2....
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You can heat "Kernel" and Choose "Restart & Run All". Then you do not need to run your codes line by line!
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