Tag: machine-learning

Building a basic prediction model with the output being the sum of the two inputs but accuracy score is significantly low

I have a csv of size 12500 X 3. The first two columns (A and B) are inputs and the the final column (C) is the sum of the two columns. I wanted to build a prediction model to get the value of C for a given A and B. This is just a basic model to imporve my understanding

List comprehension in keras custom loss function

deep-learning machine-learning python tensorflow

I want to make my custom loss function. First, the model’s output shape is (None, 7, 3). So I want split the output to 3 lists. But I got an error as follows: I think upper_b_true = [m[0] for m in y_true] is not supported. I don’t know how to address this problem. I tried to execute it while partially

ValueError:Reshape your data either using array.reshape(-1, 1)if your data has a single feature or array.reshape(1, -1) if it contains a single sample

fastapi linear-regression machine-learning python python-3.x

**the code predict the house price with polynomial regression model and fastapi **` make class have an one parameter and have a 4 value #The train_plynomial_model is a function that takes the Features and returns polynomial model The predict is a function that predict the house price “ I tried to put this sentencenewfeature=newfeature.reshape(-1, 1) Answer You should change features

How to integrate two models in sequential order in one endpoint?

amazon-sagemaker machine-learning python

I want to create an API using one endpoint but in my app, the first model output is the second model output. Is it possible to implement using SageMaker? From my understanding model_fn, prediction_fn, output_fn can only use one model at a time. Answer Pipeline Model (sequential models) There is a specific mode in SageMaker: Look at PipelineModel. You can

ValueError: Invalid classes inferred from unique values of `y` in XGBoost

machine-learning pandas python xgboost

I’m new to the Data Science field and I’m trying to apply XGBoost in a table having 5 rows × 46 columns and my last column is my target column. and the error I’m getting is Can anyone help me with the resolution? Answer I think you need to have the class numerotated from 0 to n-1 where n is

How to set the funcion timeout for Genetic Algorithm to more than 10 sec?

genetic-algorithm machine-learning optimization python timeout

I have a loss function that opens an external .exe file for calculation, and running this executable file usually takes around 2 or 3 minutes. It works very well, but When I want to pass this function to the Genetic Algorithm for optimization, I face this error: So it obviously says that GA waited 10 sec and did not receive

Can evaluation metrics for test set be better than training set?

logistic-regression machine-learning python

I am doing some Logistics Regression homework. I just wonder if in any case, the evaluation metrics for the test set are a bit better than the training set (like my results below)? And if yes, what gap is allowed? Below is my evaluation result for the test set and training set, given that both sets are extracted from the

Does my learning curves show overfitting? [closed]

classification machine-learning neural-network overfitting-underfitting python

Closed. This question is not about programming or software development. It is not currently accepting answers. This question does not appear to be about a specific programming problem, a software algorithm, or software tools primarily used by programmers. If you believe the question would be on-topic on another Stack Exchange site, you can leave a comment to explain where the

How to make a custom gradient for a multiple output function?

backpropagation machine-learning python tensorflow

I would like to know how to write a custom gradient for a function which have multiple outputs( or an array). For a simple example, I wrote the following code for y=tan( x @ w + b) with x shape is (2,3) and y shape is (2,2). To compare results, I calculated the operation by usual way and by the

Should I separate my data into different batches and then perform tsne on each batch?

dimensionality-reduction machine-learning python tsne

I have a very huge dataset and required to reduce the embedding of 768 dimension to 128dimension with TSNE. Since I have more than 1million rows, it takes more than weeks to complete dimension reduction on whole dataset, so I thought maybe I can separate the dataset into different parts and then perform each part separately. I do not have