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(Page créée avec « = Neural networks = ») |
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= Neural networks = | = Neural networks = | ||
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| + | == Pre-processing == | ||
| + | |||
| + | * Data exploration (clean-up) | ||
| + | * Data transformation | ||
| + | * Outlier detection and removal | ||
| + | * Data normalization, one of: | ||
| + | ** Min-Max Normalization, min-max normalization is a linear scaling algorithm. It transforms the original input range into a new data range (typically 0-1). | ||
| + | ** Zscore Normalization, in Zscore normalization, the input variable data is converted into zero mean and unit variance. | ||
| + | ** Sigmoidal Normalization, sigmoidal normalization is a nonlinear transformation. It transforms the input data into the range -1 to 1, using a sigmoid function | ||
| + | * Data analysis | ||
| + | * Validation of results | ||
Version du 12 avril 2019 à 11:00
Neural networks
Pre-processing
- Data exploration (clean-up)
- Data transformation
- Outlier detection and removal
- Data normalization, one of:
- Min-Max Normalization, min-max normalization is a linear scaling algorithm. It transforms the original input range into a new data range (typically 0-1).
- Zscore Normalization, in Zscore normalization, the input variable data is converted into zero mean and unit variance.
- Sigmoidal Normalization, sigmoidal normalization is a nonlinear transformation. It transforms the input data into the range -1 to 1, using a sigmoid function
- Data analysis
- Validation of results