Tag: machine-learning

ValueError: Classification metrics unable to handle multiclass

I am trying to build a object classification model, but when trying to print out the classification report it returned a value error. ValueError: Classification metrics can’t handle a mix of multiclass and continuous-multioutput targets This is my current code: However, when I attempt to print out the classification report, it ran into this error: Answer Your problem is caused

How can I print the training and validation graphs, and training and validation loss graphs?

conv-neural-network deep-learning machine-learning python tensorflow

I need to plot the training and validation graphs, and trarining and validation loss for my model. Answer history object contains both accuracy and loss for both the training as well as the validation set. We can use matplotlib to plot from that. In these plots x-axis is no_of_epochs and the y-axis is accuracy and loss value. Below is one

Transfer Learning with Quantization Aware Training using Functional API

deep-learning keras machine-learning python tensorflow

I have a model that I am using transfer learning for MobileNetV2 and I’d like to quantize it and compare the accuracy difference against a non-quantized model with transfer learning. However, they do not entirely support recursive quantization, but according to this, this method should quantize my model: https://github.com/tensorflow/model-optimization/issues/377#issuecomment-820948555 What I tried doing was: It is still giving me the

Machine Learning question (Solving ValueError: could not convert string to float:)

machine-learning python

I am running the example code Below: How the CSV looks **-Using a csv with the name phishing_site_urls.csv, that has two columns one named “URL” and the other “Label”. Where the URL column holds links that are either phishy or valid and the label column has a corresponding “bad” or “good” for determining which link in the URL column is

How to create tensorflow dataset from runtime generated images?

keras machine-learning python tensorflow

So, I start a small project based on tensorflow and could not understand how to prepare dataset generated from memory input. I have a random number of sources, that generate images. Than I pass it to python script. Images created as bytes array with png format. I collect images to array, and want to prepare dataset from it and train

How do I make sure GridSearchCV first does the cross split and then the imputing?

data-preprocessing gridsearchcv machine-learning python scikit-learn

I have a GridSearchCV, with a pipeline that looks something like this: my GridSearchCV looks like this: with Cross Validation = 5 So, how do I ensure that I split the data first, and then impute in the most frequent? Answer GridSearchCV will run roughly like this: You can be sure that SimpleImputer and StandardScaler will do .fit() and .transform()

Confuse why my KNN code is throwing a ValueError

knn machine-learning pandas python scikit-learn

I am using sklearn for KNN regressor: I get this error message: Could someone please explain this? My data is in the hundred thousands for target and the thousands for input. And there is no blanks in the data. Answer Before answering the question, Let me refactor the code. You are using a dataframe so you can index single or

How to input embeddings directly to a huggingface model instead of tokens?

huggingface-transformers machine-learning python pytorch

I’m going over the huggingface tutorial where they showed how tokens can be fed into a model to generate hidden representations: But how can I input word embeddings directly instead of tokens? That is, I have another model that generates word embeddings and I need to feed those into the model Answer Most (every?) huggingface encoder model supports that with

SHAP Linear model waterfall with KernelExplainer and LinearExplainer

classification dataframe machine-learning python shap

I am working on binary classification and trying to explain my model using SHAP framework. I am using logistic regression algorithm. I would like to explain this model using both KernelExplainer and LinearExplainer. So, I tried the below code from SO here This threw an error as shown below AssertionError: Unknown type passed as data object: <class ‘shap.maskers._tabular.Independent’> How can

How to properly use Smote in Classification models

data-science jupyter-notebook machine-learning python scikit-learn

I am using smote to balanced the output (y) only for Model train but want to test the model with original data as it makes logic how we can test the model with smote created outputs. Please ask anything for clarification if I didn’t explained it well. It’s my starting on Stack overflow. Here i applied the Random Forest Classifier