How do you select and transform features for neural networks?

1 Why is feature engineering important?

Feature engineering is important because it can affect the accuracy, speed, and robustness of your neural network. By selecting and transforming the features that capture the essence of your data, you can reduce the noise, redundancy, and dimensionality of your input. This can help your neural network learn faster, generalize better, and avoid overfitting. Feature engineering can also help you deal with missing values, outliers, and imbalanced data, which are common challenges in predictive modeling.

2 How to select features for neural networks?

Selecting features for neural networks involves finding the variables that have a strong relationship with your target variable and removing the ones that are irrelevant or redundant. There are different methods to do this, such as correlation analysis, mutual information, feature importance, and wrapper methods. Correlation analysis measures the linear association between two variables, while mutual information measures the amount of information shared by two variables. Feature importance ranks the features based on their contribution to the model's performance, while wrapper methods use a subset of features and evaluate them with a cross-validation score. You can use these methods to filter out the features that have low correlation, low mutual information, low importance, or low score with your neural network.

3 How to transform features for neural networks?

Transforming features for neural networks involves applying some operations or functions to the input variables to make them more suitable for your model. There are different types of transformations you can use, such as scaling, encoding, normalization, standardization, and binning. Scaling adjusts the range of the features to a common scale, such as 0 to 1 or -1 to 1. Encoding converts categorical features into numerical values, such as one-hot encoding or label encoding. Normalization adjusts the distribution of the features to a normal or Gaussian distribution, while standardization adjusts the mean and standard deviation of the features to zero and one, respectively. Binning groups continuous features into discrete intervals or bins, such as equal-width or equal-frequency binning. You can use these transformations to make your features more compatible with your neural network's activation functions, optimization algorithms, and regularization techniques.

4 How to test and evaluate your features?

Testing and evaluating your features is an essential step to check the quality and effectiveness of your feature engineering. You can use different metrics and methods to do this, such as visualizations, statistics, and model performance. Visualizations can help you explore the distribution, relationship, and variation of your features, such as histograms, scatter plots, and box plots. Statistics can help you measure the descriptive and inferential properties of your features, such as mean, median, standard deviation, skewness, kurtosis, and p-value. Model performance can help you compare the accuracy, precision, recall, and F1-score of your neural network with different sets of features. You can use these metrics and methods to identify the best features for your neural network and fine-tune your feature engineering.

5 How to automate and optimize your feature engineering?

Automating and optimizing your feature engineering can save you time and effort and improve your results. There are different tools and techniques you can use to do this, such as feature selection algorithms, feature extraction algorithms, and autoencoders. Feature selection algorithms can automatically select the best subset of features from a large pool of candidates, such as recursive feature elimination (RFE), sequential feature selection (SFS), or genetic algorithms (GA). Feature extraction algorithms can automatically create new features from the existing ones by applying some transformations or combinations, such as principal component analysis (PCA), linear discriminant analysis (LDA), or kernel methods. Autoencoders are neural networks that can automatically learn a compressed representation of the input data by encoding and decoding it, which can be used as a new set of features for your neural network. You can use these tools and techniques to simplify and enhance your feature engineering.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?