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
Tag: classification
How to visualize Classification using pandas and matplotlib?
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]
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Python : y should be a 1d array, got an array of shape {} instead. format(shape)
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?
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
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
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
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
Suspect overfitting binary classification toy problem with scikit-learn RandomForestClassifier
I’m trying to train a Random Forest to classify the species of a set of flowers from the iris dataset. However, the validation looks kind of weird to me, since it looks like the results are perfect, which is something I would not expect. Since I would like to perform a binary classification, I exclude from the training dataset the
AttributeError: ‘numpy.ndarray’ object has no attribute ‘self’
I started implementing the backend of neural network but got stuck in a code of python. The below is the code for neural Network. While i was making use of the userdefined class in one of the application to be made, i got an error by name attributeError. Please help me out solving it. I […]