increasing the input dataset and finding the corresponding increased output by training the current input and outputs
The input excel file(trainn.xlsx) represents the car percentage and the output excel file(pred.xlsx) represents the different output values from input values.There are 160 inputs and 160 outputs.I want to generate more 340 inputs and corresponding 340 outputs(for every column) by training the current dataset.Is it possible to increase the values to 500 for both input and output by generating new datas??My current code:
Classification on machine learning algorithms
I have been studying machine learning for past three months and came across the statement “Machine learning models can be classified as generative or descriptive, probabilistic vs non-probabilistic and parametric vs non-parametric.”
Getting high loss on the model that I created
I can share the full data on the stack, so I would just share the code below.
I have been trying for the past few days on this data but it seems that the loss is coming around 18000 or 15000 which is to high. My colleagues told me to use neural network for this data.
Is machine learning good for predicting values like production speed?
I wonder if machine learning would be a good way to predict production performance meters (like machine speed pcs/h, machine setup time, waste). The product is carton boxes, so quite simple thing from construction point (easy to classify)
The scenario is that there are several types of machines, different designs of boxes (which can be grouped into lets say 10 types), so those parameters are not numeric, then we have numeric parameters like carton grammage, box width,length, height, number of colors. So I am looking to train a model which after providing machine type, box type, grammage, width,length etc would predict machine setup time, runnng speed i pcs/h, waste %.
If machine learing is a good tool for that purpose then please can you tell me which kind of models would suit the best.
Are the predictions made by LLMs based on probability?
I’m studying machine learning and will like to understand how LLMs make their predictions. Are these predictions probabilistic or they are absolute truth.
Affinity propagation in scikit-learn doesn’t converge
I did an affinity propagation algorithm and got above 100 clusters, but evaluating with different methods, I get 3 clusters.
How is machine learning incorporated into search engine design?
I am currently building a small in-house search engine based on Apache Lucene. Its purpose is simple – based on some keywords, it will suggest some articles written internally within our company. I am using a fairly standard TF-IDF scoring as a base metric and built my own scoring mechanism on top it. All of these seem to be working excellent except for some corner cases where the ranking seems messed up.
How is machine learning incorporated into search engine design?
I am currently building a small in-house search engine based on Apache Lucene. Its purpose is simple – based on some keywords, it will suggest some articles written internally within our company. I am using a fairly standard TF-IDF scoring as a base metric and built my own scoring mechanism on top it. All of these seem to be working excellent except for some corner cases where the ranking seems messed up.
How to make the leap from classification to clustering
I have a clustering problem which I can’t seem to solve, although if I treat it as a labeled classification problem, I can solve it with satisfactory precision. Is there an elegant way to make the leap from being able to solve the classification problem, to being able to solve the clustering one?
How to make the leap from classification to clustering
I have a clustering problem which I can’t seem to solve, although if I treat it as a labeled classification problem, I can solve it with satisfactory precision. Is there an elegant way to make the leap from being able to solve the classification problem, to being able to solve the clustering one?