With regard to time series features in a regression ML model. Suppose, we are living in a space colony. The temperature there is accurately under control, so we
I was wondering how the final model (i.e. decision boundary) of LogisticRegressionCV in sklearn was calculated. So say I have some Xdata and ylabels such that
""" Defining two sets of inputs Input_A: input from the features Input_B: input from images my train_features has (792,192) shape my train_images has (792,28,28
I want to compare 2 date and predict a label true if date 1 greater than date 2 and predict false date 1 less than date 2. I have trained the model but model is
I need to avoid downloading the model from the web (due to restrictions on the machine installed). This works, but it downloads the model from the Internet mode
In the example below, pipe = Pipeline([ ('scale', StandardScaler()), ('reduce_dims', PCA(n_components=4)), ('clf', SVC(kernel = 'linear
I am trying to train a decision tree model, save it, and then reload it when I need it later. However, I keep getting the following error: This DecisionTre
Is there any way to use multiple time-series to train one model and use this model for predictions given a new time-series as an input? It is rather a theoretic
I am currently performing multi class SVM with linear kernel using python's scikit library. The sample training data and testing data are as given below: Mode
I am using some text for some NLP analyses. I have cleaned the text taking steps to remove non-alphanumeric characters, blanks, duplicate words and stopwords, a
My Data set contains categorical variables so I am using label encoding and one hot encoder and my code is as follows can I use a loop to ensure that my cod
I do not understand why do I get the error KeyError: '[ 1351 1352 1353 ... 13500 13501 13502] not in index' when I run this code: cv = KFold(n_splits=10) fo
CODE import numpy as np import cv2 from google.colab.patches import cv2_imshow img=cv2.imread('/gdrive/My Drive/Colab Notebooks/merlin_190860876_3e2e2660-237f-4
I am trying to save the the weights of a pytorch model into a .txt or .json. When writing it to a .txt, #import torch model = torch.load("model_path") string =
How do you compute the true- and false- positive rates of a multi-class classification problem? Say, y_true = [1, -1, 0, 0, 1, -1, 1, 0,
How do you compute the true- and false- positive rates of a multi-class classification problem? Say, y_true = [1, -1, 0, 0, 1, -1, 1, 0,
Currently I have a dataset below and I try to accumulate the value if ColA is 0 while reset the value to 0 (restart counting again) if the ColA is 1 again. Col
In my CNN model I want to extract X_train and y_train from train_generator. I want to use ensemble learning, bagging and boosting to evaluate the model. the mai
I am developing a mini autonomous car using 3 CNNs and a camera sensor using this approach. One of the CNNs detects lanes on the images and outputs images wit
I'm trying to build a model which can be trained on both audio and video samples but I get this error ValueError: Please initialize `TimeDistributed` layer with