Tag: classification

SKLearn & ElasticNet: Cross validation fails when using Accuracy as a metric

classification elasticnet python scikit-learn

I have a binary classification problem. I’ve been using cross validation to optimize the ElasticNet parameters. However ElasticNet only seems to work when I supply roc_auc as the scoring method to be used during CV, However I also want to test out a wide range of scoring methods, in particular accuracy. Specifically, when using accuracy, ElasticNet returns this error: However

How to visualize Classification using pandas and matplotlib?

classification dataframe matplotlib pandas python

I’m trying to classify a dataset using python with pandas. The iris flower dataset consists of 50 samples from each of three species of Iris and contains four features.The goal is to distiguish between the species of irises based on these features. Question: Generate a scatter plot with sepal_length feature in x-axis and petal_width feature in y-axis, which shows 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

Python : y should be a 1d array, got an array of shape {} instead. format(shape)

classification data-mining python regression

The above is my code which I tried in Google Colab. But here it shows one error : This is error is shown in the line Please help me to solve this error. I am a beginner so answer the question with elaboration Answer Your problem is that the outputs of train_test_split are ordered differently than you think. train_test_split returns

How can we make a prediction using Scikit-Learn Classifiers?

classification data-science machine-learning python scikit-learn

I can easily train and test a classifier using the code below. Now, how can I make a prediction of the target variable (dependent variable) based on the independent variables? Something like this should work, I think, but it doesn’t… If we leave numerics as numerics, and put quotes around labels, I would like to predict the dependent variable, but

RuntimeError: Found dtype Char but expected Float

classification deep-learning machine-learning python pytorch

I am using PyTorch in my program(Binary Classification). The output from my model and actual labels are When I calculate the Binary Cross Entropy, it gives me the error I have no idea how it is finding the Char dtype. Even If calculate it manually, it gives me this error. My DataLoader is my training loop is And my Model

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 add a traditional classifier(SVM) to my CNN model

classification conv-neural-network keras python svm

here’s my model i want to make svm classifier as my final classifier in this model so how can i do that? also another question i want to know the predicted class of a certain input so when i use it only gives me probabilities so how can i solve that too Answer You can use neural network as feature

Training, Validation and Test sets for imbalanced datasets in Machine Learning

classification machine-learning nlp python scikit-learn

I am working on an NLP task for a classification problem. My dataset is imbalanced and some authors have only 1 text, and thus I want to have this text only in the training set. As for the other authors I need to split the dataset into 70% training set, 15% validation set and 15% test set. I tried to

Python Tensorflow Shape Mismatch (WaveNet)

classification keras librosa python tensorflow

I was trying to run a WaveNet, which is specified in https://github.com/mjpyeon/wavenet-classifier/blob/master/WaveNetClassifier.py. Part of my code is as follows: Here, self.input_shape=X_train.shape and self.output_shape=(11,) It successfully printed out the model’s summary, but was outputting the following error: However, my X_train has a shape of (19296, 110250). I was trying to figure out on why the X_train has been reshaped from (19296,