I use xgboost to do a multi-class classification of spectrogram images(data link: automotive target classification). The class number is 5, training data includ
I am working on a Model for Machine Learning and was able to generate the scores of the processes. I am not sure how to use them to make a decision on which is
I trained my tensorflow model on images after convert it to BatchDataset IMG_size = 224 INPUT_SHAPE = [None, IMG_size, IMG_size, 3] # 4D input model.fit(
I am using a scikit-learn pipeline with XGBRegressor. Pipeline is working good without any error. When I am prediction with this pipeline, I am predicting the
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,