Category "machine-learning"

convert Kitti labels to Yolo

Trying to convert Kitti label format to Yolo. But after converting the bbox is misplaced. this is kitti bounding box This is conversion code: def convertToYolo

Is this a valid approach to scale your target in machine learning without leaking information? [closed]

Consider a housing price dataset, where the goal is to predict the sale price. I would like to do this by predicting the "Sale price per Squar

Regarding Multiple ROIs in an image using python/opencv

When you select multiple ROIs in an image, how to display them all simultaneously in that image white selecting one by one? How to deselect some selected ROIs i

AttributeError: 'LinearRegression' object has no attribute 'coef_'

I am self-studying machine learning and python. I am using sklearn and I want to plot the regression line, but I get the attributeError: 'LinearRegression' obje

Attribute Error on predicting the image 'DirectoryIterator' object has no attribute 'Filepath'

I am working on a CNN architecture with an image RGB dataset that belongs to two categories, i.e., crops and another one is grass. However, I am concerned about

seasonal_periods has not been provided and index does not have a known freq. You must provide seasonal_periods

When trying to train a model with Darts I get these ValueErrors: "ValueError: seasonal_periods has not been provided and index does not have a known freq. You m

Remove blank rows and columns of an array inside a keras Sequential model

I have a keras model, which takes a 10x10x1 array as input. For example: array = np.array([ [[0],[0],[0],[0],[0],[0],[0],[0],[0],[0]], [[0],[0],[0],[0],[0],[0],

fastai show_batch and show_results does nothing

I build a model on fastai v2.3.1. When I try to call functions show_batch and show_results it does not show anything. Here is the problematic code: from fastai.

tensorflow keras fit not showing any information

When using model.fit(), I only see the text "Epoch 1/10", and after a few seconds, it shows "Finished" with exit code: -1073740791 status: 1. My labels are a on

Remove variable in model_parts() plot

I want to remove certain variables from the plot. # Packages library(tidymodels) library(mlbench) # Data data("PimaIndiansDiabetes") dat <- PimaIndiansDia

Gradient Descent for Linear Regression in One Variable(Octave)

Can anybody give a detailed explaination on how this piece of code works? h=(theta' * X')'; theta = theta -((1/m) * (h - y)' * X)' * alpha; *where X is the fea

Using softmax for multilabel classification (as per Facebook paper)

I came across this paper by some Facebook researchers where they found that using a softmax and CE loss function during training led to improved results over si

Use Index of the Loadings (IL) method to calculate the optimal number of principal components

I want to use Index of the Loadings method to calculate the optimal number of principal components using the index_of_loadings_matrix function. My dataframe was

Weird behaviour when finetuning Huggingface Bert model with Tensorflow

I am trying to fine tune a Huggingface Bert model using Tensorflow (on ColabPro GPU enabled) for tweets sentiment analysis. I followed step by step the guide on

ClassValuePicker in Weka KnowledgeFlow doesn't seem to let you pick a class

Does anyone have any insight? New to using Weka KnowledgeFlow but I've followed the official documentation to a t. I'm trying to generate multiple ROC plots, wh

Talos, syntax to select best_model

I'm running a toy model for learning, on Ubuntu 21.10, in a conda environment that comprises python 3.74, keras 2.4.3 and talos 1.0, among many other packages.

Deploying ML model on Streamlit

I have a code that basically takes in a csv, which can be uploaded from streamlit and then pushes out a classification prediction. Just as a context I use xgboo

am getting NotFittedError: This MultinomialNB instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator

my code is import streamlit as st import pickle import string from nltk.corpus import stopwords import nltk from nltk.stem.porter import PorterStemmer ps = Port

How to train a model to predict picks using multiple independent dataframes?

I have a dictionary of 100+ dataframes all have the same shape [9999 rows x 4 columns] with the following columns: ['time', 'response', 'arrival_1','arrival_2']

How to train a model to predict picks using multiple independent dataframes?

I have a dictionary of 100+ dataframes all have the same shape [9999 rows x 4 columns] with the following columns: ['time', 'response', 'arrival_1','arrival_2']