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#Loading datax, y = load_data(); scaler = MinMaxScaler(feature_range=(0,1)) x, y = scaler.fit_transform(x), scaler.fit_transform(y) train_x, train_y = x[0:int(math.floor(len(x)*.67)), :], y[0:int(math.floor(len(y)*.67))]#Creating weights and biases dictionariesweights...
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fit(train_data[di:di+smoothing_window_size,:]) train_data[di:di+smoothing_window_size,:] = scaler.transform(train_data[di:di+smoothing_window_size,:]) # You normalize the last bit of remaining data scaler.fit(train_data[di+smoothing_window_size:,:]) train_data[di+smoothing_window_...
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Robust Scaling:Uses median and interquartile range to scale features, which is less sensitive to outliers.pythonCopy codefrom sklearn.preprocessing import RobustScaler scaler = RobustScaler() scaled_data = scaler.fit_transform(data) 6. What is cross-validation, and why is it important?
X_test_scaled = scaler.transform(X_test) # Perform LassoCV to find the optimal alpha lasso_cv = LassoCV(cv=5, random_state=42) lasso_cv.fit(X_train_scaled, y_train) # The optimal alpha best_alpha = lasso_cv.alpha_ print(f'Optimal alpha: {best_alpha}') # Apply Lasso with the...