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Results tagged with feature-engineering
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user 41908
the process of using domain knowledge of the data to create features that improve machine learning algorithms
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How to add lagging Features for Forecasting with a random lag range without adding a new col...
The common way of adding lagging features in time series forecasting problems is adding lag columns with pandas.shift(). While it is a fine method but what about when wanting to use a random integer ( …
- 151
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Accepted
Can Machine Learning Algorithms Process Contextual Features for Regression?
Will borrow Luca Morin's answer from the comments :
"yeah some algo can't learn that and need feature engineering. And some algo can, typically NN are universal function approximators (see: en.wikiped …
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Can Machine Learning Algorithms Process Contextual Features for Regression?
Take Figure 1 showing point interpolation, where point L0 is being interpolated using points L2 and L1 and the distances L11, L12, L21, and L22.
Whilst the graph shows a linear interpolation example …
- 151
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How to add lagging Features for Forecasting with a random lag range without adding a new col...
Creating the DataFrame
df = pd.DataFrame({'x': x, 'y': y})
Applying different random shift for each observation in y.
number_of_lags = 10
df['lag_shift_value'] = [random.randint(1, number_of_la …
- 151
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How to scale exponential data for a regression problem?
import yfinance as yf
# Define the ticker symbol
tickerSymbol = 'NVDA'
# Get data on this ticker
tickerData = yf.Ticker(tickerSymbol)
# Get the historical prices for this ticker
# '1m' interval fo …
- 151