Topic 1 Question 26
A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data. Which technique for feature engineering should the ML engineer use for the model?
Apply label encoding to the color categories. Automatically assign each color a unique integer.
Implement padding to ensure that all color feature vectors have the same length.
Perform dimensionality reduction on the color categories.
One-hot encode the color categories to transform the color scheme feature into a binary matrix.
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- 正解だと思う選択肢: D
- Label Encoding: Ordinal relationship
- Padding: Sequence data
- Dimensionality Reduction: High-dimensional data
- One-Hot Encoding: Categorical data (Right)
👍 3Saransundar2024/12/04 - 正解だと思う選択肢: D
One-hot encoding creates a new binary feature for each unique category (color in this case). For example, if there are three colors (red, blue, green), one-hot encoding would create three binary columns like this: Red: [1, 0, 0] Blue: [0, 1, 0] Green: [0, 0, 1] this way, the model can work with the color feature without assuming ordinal relationships between colors.
👍 1GiorgioGss2024/11/27
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