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It is a machine learning model which predicts the house price in Bangalore.
This model is trained using a dataset that contains information about 13321 houses. However, this dataset has a lot of miss…
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Hi all, I started doing the ml project in chapter 2 in these 1-2 months.
I checked my code for serveral times and they are more or less the same as this repo.
I get the following results
```…
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### Description
Mesh tuning currently takes way too long especially on large number of nodes (>= 2 nodes). We can bypass this if a prediction can be made based on a linear interpolation of mesh sizes…
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I am trying to fine tune the module for a specific use case and I have been having a plethora of issues that I have ultimatley "by-passed" but this one has been a headdache for a while
I have been …
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Greetings to all,
I am training a DCN model for ranking purpose. The DCN model has the below structure
```
tf.keras.Sequential([
tfrs.layers.dcn.Cross(projection_dim=None,kernel_ini…
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Bringing together notes for different cases of prediction results. This does not include conditional forecasting as in TSA, but includes non-linear and non-normal models.
example, simplest case OLS: …
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Here I want to brainstorm a list to what are all the potential threats (i.e., where can things go wrong) to a machine learning project? Our checklist need not address all of them, but we should in our…
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**Is your feature request related to a problem? Please describe.**
Yes, the current currency converter tools often provide real-time exchange rates without incorporating historical data analysis. Use…
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Hey! I am pretty new here
I have noticed in KernelSHAP's notebooks that in order to calculte the SHAP values (for example in the Iris data with SVC model) you guys used probabilities prediction:
`…
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I have 3 input dimensions (x, y, time) and 5 outputs. Below is the code I use
```
num_outputs = n_stations
n_inputs = 2
K1 = GPy.kern.Bias(input_dim=n_inputs)
K2 = GPy.kern.Linear(input_dim=n_i…