classifier | Machine learning code , derivatives calculation | Machine Learning library

I am using yellowbrick to plot the AUCROC. I want to remove the title from the plot, to make it empty without the plot title.

I have a pyTorch-code to train a model that should be able to detect placeholder-images among product-images. I didn't write the code by myself as I am very unexperienced with CNNs and Machine Learning.

My boss told me to calculate the f1-score for that model and i found out that the formula for that is ((precision * recall)/(precision + recall)) but I don't know how I get precision and recall. Is someone able to tell me how I can get those two parameters from that following code? (Sorry for the long piece of code, but I didn't really know what is necessary and what isn't)

I have the following piece of code:

I want to create a pipeline that continues encoding, scaling then the xgboost classifier for multilabel problem. The code block;

I was reading a decent paper S-DCNet and I fell upon a section (page3,table1,classifier) where a convolution layer has been used on the feature map in order to produce a binary classification output as part of an internal process. Since I am a noob and when someone talks to me about classification I automatically make a synapse relating to FCs combined with softmax, I started wondering ... Is this a possible thing to do? Can indeed a convolutional layer be used to classify a binary outcome? The whole concept triggered my imagination so much that I insist on getting answers...

Honestly, how does this actually work? What is the difference between using a convolution filter instead of a fully connected layer for classification purposes?

Edit (Uncertain answer on how does it work): I asked a colleague and he told me that using a filter of the same shape as the length-width shape of the feature map at the current stage, may lead to a learnable binary output (considering that you also reduce the #channels of the feature map to a single channel). But I still don't understand the motivations behind such a technique ..

If I need to freeze the output layer of this model which is doing the classification as I don't need it.

I'm trying to train some ML algorithms on some data that I collected, but I received an error for input variables with inconsistent numbers of samples. I'm not really sure what variables needs to be changed or not. I've posted my code below to give you a better understanding of what I'm trying to accomplish:

A similar question is already asked, but the answer did not help me solve my problem: Sklearn components in pipeline is not fitted even if the whole pipeline is?

I'm trying to use multiple pipelines to preprocess my data with a One Hot Encoder for categorical and numerical data (as suggested in this blog).

Here is my code, and even though my classifier produces 78% accuracy, I can't figure out why I cannot plot the decision-tree I'm training and what can help me fix the problem. Here is the code snippet:

I am trying to create a class for calibrating a classifier. I have been reading resources on probability calibration and I am a bit confused on which dataset should we calibrate the classifier. I created a class that split the training set to further train and validation the set. Then, the classifier is first fitted to the train set and predicts the uncalibrated probability on the validation set.

Then, I create a cal_model instance of the CalibrationCV class and then fit it to the validation set and predict calibrated probabilities of the validation set again.

Could someone take a look at the code below and correct the code for me?