I used this code and get output from this(with the help of Oxbowerce).
df4.to_excel(r'/content/Book1.xlsx', index = False)
I saved the output in an excel file. You can see in the below picture.
The outputs of your model are in the same order as your inputs, so the first row in your output array corresponds to the first row in the testX array. If you want to have both the inputs and the model prediction in one table you can just concatenate them along the column axis.
According to the sklearn.svm.SVR documentation, the negative $R^2$ value indicates that your model is arbitrarily worse than the trend line on trainY.
By default you should check the following:
Does your model have a bias/intercept? If not you may observe negative $R^2$.
Is testY derived from your training data?
Am I using a linear function to fit the data? ...
Apart from the considerations about the quality of the data or whether or not the model is suitable for the problem, one good apporach is to try different combnations of the algorithm parameteres (using cross-validation) to come up with the best possible model.
I mean, you can do a grid search or a randomizded search to find out which combination of the ...
A $R^2$ that is that low tells you that your model is not good. Therefore, you can both make it positive and nearer to 1 by :
a) getting better/more data, or
b) picking a better model for your data.
Also, it'd be more helpful to plot the true/pred values against the underlying $X$ values and not just as a sequence.
From my understanding you are working on a regression task in which you have applied MainMaxScaler to your target variable y prior modeling.
If so you have two options:
As the error message suggests, you can reshape the output with array.reshape(-1, 1)
Scikit learn has implemented a class to work with transformations on target:
So just try
You deal with a highly skewed censored distribution which makes it really hard to get good estimates. The key question is what information is available to model the skewed income distribution (you don't say anything on that).
There is quite some literature on the issue of imputing top incomes. E.g. "Measuring inequality using censored data: a multiple‐...
You can detect drift in new predictions, probably not in real-time but accumulating predictions, to be able to detect relevant drift patterns and not just outliers.
I suggest you take a look at package drifter_ml. In the list of supported approaches for classification, you can find a section called "Against New Predictions", which contains the ...
You may or may not use ground truth to detect drift.
According to google:
What is data drift? Data drift is one of the top reasons model accuracy degrades over time. For machine learning models, data drift is the change in model input data that leads to model performance degradation. Monitoring data drift helps detect these model performance issues.
Well for engagement you would want to look at likes and comments, as well as the type of comments (ex. if people tag their friends in the comments).
Depending on the social media platform the business accounts may also have access to views, i.e. how many people actually see the post or visit the profile, in which case you can then look at the proportion of ...
Most of the common libraries you would use for data manipulation do actually use C (or C++ or Fortran, etc.) under the hood.
There are even libraries such as CuPy, which offers the entire NumPy API, but can run your code on a GPU. Using GPUs for speed is a much more common use case these days (in my experience), compared to writing the C/C++ version.