What is feature engineering in neural networks

This recipe explains what is feature engineering in neural networks

Recipe Objective - What is Feature Engineering in neural network?

Feature Engineering is a technique of creating new features or variables using the features already present in the data. Feature engineering is done after doing hypothesis testing that is creating various hypothesis without seeing the data thus avoiding biased model afterwards. Features created during feature engineering improves model accuracy, model performance on new data, decrease model loss thereby adding business value. Features are created out of brainstorming ideas, divisive techniques like automatic feature extraction etc, Selecting features using feature selection technique etc.

This recipe explains what is Feature Engineering, how it is beneficial for neural network models and how it can be executed.

FastText and Word2Vec Word Embeddings Python Implementation

Explanation of Feature Engineering.

Imputation is a technique of using feature engineering. It involves numerical execution, categorical execution or random sample imputation. Numerical imputation involves replacing missing values with a default numerical value rather than dropping the whole column. Categorical imputation involves the replacement of the missing values with maximum occurred value in a column so as to increase the model accuracy. Random sample imputation involves taking the random observation from a dataset and using that observation to replace the NaN values which helps in decreasing the model loss.

Binning is a technique of using feature engineering. It involves taking a column as an input with continuous numbers and placing the numbers in bins based on the range that is predetermined by us. The output gives the new categorical variable feature. It helps in preventing overfitting and improving model accuracy.</p

Log Transform is a technique of using feature engineering. It involves handling of skewed data and after transformation, distribution becomes more approximate to the normal. It decreases the effect of outliers due to normalization of magnitude differences and the model become more robust and effective. Log transform only requires positive data as an input and if negative data is given as input, it will give an error.

Log Transform is a technique of using feature engineering. It involves handling of skewed data and after transformation, distribution becomes more approximate to the normal. It decreases the effect of outliers due to normalization of magnitude differences and the model become more robust and effective. Log transform only requires positive data as an input and if negative data is given as input, it will give an error.

One-hot encoding is a technique of using feature engineering. It is most common encoding method. It involves spreading values in a column to multiple flag columns and assigning 0 or 1 to them. These binary values expresses a relationship between grouped and the encoded column. The technique changes the categorical data to the numerical format and enables to group the categorical data without losing any important information.

Scaling is a technique of using feature engineering. Normalization and Standardization are the two scaling processes. Normalization scales all the values in the fixed range between 0 and 1 and transformation does not change the distribution of feature so, due to the decrease in standard deviations, effects of the outliers increases. Standardization or z-score normalization scales values while taking into account the standard deviation. If standard deviation of features are different than their range also would differ from each other and this reduces effect of outliers in features.

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