Category : machine-learning

I’m perfoming a LinearRegression model with a pipeline and GridSearchCV, i can not manege to make it to the coefficients that are calculated for each feature of X_train. mlr_gridsearchcv = Pipeline(steps =[(‘preprocessor’, preprocessor), (‘gridsearchcv_lr’, GridSearchCV(TransformedTargetRegressor(regressor= LinearRegression(), func = np.log,inverse_func = np.exp), param_grid=parameter_lr, cv = nfolds, scoring = (‘r2′,’neg_mean_absolute_error’), return_train_score = True, refit=’neg_mean_absolute_error’, n_jobs = -1))]) ..

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I am quite new to ML and signal processing and wanted to ask for help with validating my RF model. I am analysing acoustic data in search for a particular signal. Labelled data and run the code to get RF algorithm: data = pd.read_csv(‘C:/Users/m/Desktop/nilesh_cooper/coding/test_data/features.csv’, index_col=0) from sklearn.model_selection import train_test_split #setting features and labels datasets within ..

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I have a simple NN: import torch import torch.nn as nn import torch.optim as optim class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.fc1 = nn.Linear(1, 5) self.fc2 = nn.Linear(5, 10) self.fc3 = nn.Linear(10, 1) def forward(self, x): x = self.fc1(x) x = torch.relu(x) x = torch.relu(self.fc2(x)) x = self.fc3(x) return x net = Model() opt = ..

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