Advanced Feature Engineering for Improved Model Performance

Introduction

Feature engineering is the process of using domain knowledge to extract features from raw data to improve the performance of machine learning models. Advanced feature engineering techniques can significantly enhance the predictive power of your models. These advanced techniques can be learned by enrolling for an advanced Data Science Course in Bangalore and such technical hubs of the country where specialised courses are offered by technical institutes.

This article delves into advanced feature engineering strategies, their importance, and practical applications.

Understanding Feature Engineering

Feature engineering involves transforming raw data into meaningful features that represent the underlying problem better for the model. The goal is to make the model’s job easier by providing it with well-crafted features. Advanced feature engineering goes beyond simple transformations and involves sophisticated techniques that require a deep understanding of the data and the problem domain. Hence, before you enrol for Data Scientist Classes  that cover this topic, ensure that you already have the required domain knowledge and technical background to delve into this technology.

Key Techniques in Advanced Feature Engineering

    

Polynomial Features

• Creating interaction terms and polynomial features can capture non-linear relationships between variables.
• Example: For features x1x_1x1​ and x2x_2x2​, generate x12x_1^2×12​, x22x_2^2×22​, and x1×x2x_1 \times x_2x1​×x2​.

  Log Transformations

• Applying logarithmic transformations to skewed data can stabilise variance and make the data more normally distributed.
• Example: Transform xxx to log⁡(x+1)\log(x+1)log(x+1) to handle skewness.

  Binning

• Converting continuous variables into categorical ones by binning can help in capturing non-linearities.
• Example: Bin age groups into categories like ‘0-18′, ’19-35′, ’36-50′, ’50+’.

  Feature Splitting

• Splitting features into multiple components can reveal hidden patterns.
• Example: Split datetime into year, month, day, hour, and minute components.

  Aggregations and Group Statistics

• Computing aggregate statistics (mean, median, sum) within groups can capture important patterns.
• Example: Calculate the mean purchase amount per user or median transaction time per day.

  Feature Encoding

• Encoding categorical features using techniques like target encoding, frequency encoding, or one-hot encoding.
• Example: Replace categories with the mean target value for each category (target encoding).

  Time-based Features

• For time series data, generate lag features, rolling statistics, and differences.
• Example: Create lag features like lag1\text{lag}_1lag1​ (value of the previous time step) and rolling mean of the last 7 days.

  Dimensionality Reduction

• Techniques like PCA (Principal Component Analysis) and t-SNE (t-distributed Stochastic Neighbour Embedding) can reduce feature space while preserving important information.
• Example: Apply PCA to reduce a high-dimensional dataset to its top principal components.

  Interaction Features

• Manually creating features that represent interactions between variables.
• Example: Interaction between ‘age’ and ‘income’ might be a good predictor of spending behaviour.

  Feature Selection

• Using methods like Recursive Feature Elimination (RFE), LASSO, and tree-based feature importance to select the most relevant features.
• Example: Apply RFE with a model to recursively remove less important features.

Note that advanced feature engineering is a sophisticated technology and comprises several techniques. Discussing all these techniques is beyond the scope of this article. However, there are several technical learning centres that offer elaborate courses on this topic. You can for instance, enrol for a Data Science Course in Bangalore, Chennai, Pune and such cities to gain comprehensive knowledge of the techniques used in advanced feature engineering. 

Practical Applications

Let us explore some practical applications of these techniques using a hypothetical dataset for a retail business predicting customer churn. Most professional Data Scientist Classes will include hands-on project assignments on implementing the features that constitute practical applications of  advanced feature engineering. Some of these are demonstrated in the following sections.

• Polynomial Features:
  • Create polynomial and interaction features between ‘age’ and ‘average monthly spend’:

from sklearn.preprocessing import PolynomialFeatures

poly = PolynomialFeatures(degree=2, interaction_only=True)

poly_features = poly.fit_transform(df[[‘age’, ‘avg_monthly_spend’]])

• Log Transformation:
  • Apply log transformation to the ‘tenure’ feature:

df[‘log_tenure’] = np.log(df[‘tenure’] + 1)

• Binning:
  • Bin ‘tenure’ into categorical intervals:

df[‘tenure_bin’] = pd.cut(df[‘tenure’], bins=[0, 12, 24, 36, 48, 60], labels=[‘0-1y’, ‘1-2y’, ‘2-3y’, ‘3-4y’, ‘4-5y’])

• Aggregations:
  • Calculate mean and median spend per customer group:

df[‘mean_spend_per_group’] = df.groupby(‘customer_group’)[‘avg_monthly_spend’].transform(‘mean’)

df[‘median_spend_per_group’] = df.groupby(‘customer_group’)[‘avg_monthly_spend’].transform(‘median’)

• Feature Encoding:
  • Apply target encoding to ‘customer_segment’:

target_mean = df.groupby(‘customer_segment’)[‘churn’].mean()

df[‘customer_segment_encoded’] = df[‘customer_segment’].map(target_mean)

• Time-based Features:
  • Create lag features for ‘monthly_spend’:

df[‘monthly_spend_lag_1’] = df[‘monthly_spend’].shift(1)

df[‘monthly_spend_lag_2’] = df[‘monthly_spend’].shift(2)

• Dimensionality Reduction:
  • Apply PCA to reduce dimensionality of high-dimensional features:

from sklearn.decomposition import PCA

pca = PCA(n_components=5)

pca_features = pca.fit_transform(df[high_dim_features])

• Interaction Features:
  • Create interaction between ‘age’ and ‘income’:

df[‘age_income_interaction’] = df[‘age’] * df[‘income’]

• Feature Selection:
  • Use RFE for feature selection:

from sklearn.feature_selection import RFE

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()

rfe = RFE(model, 10)

fit = rfe.fit(df[features], df[target])

selected_features = fit.support_

 

df_selected = df[features].iloc[:, selected_features]

Conclusion

Advanced feature engineering is crucial for extracting valuable insights from data and improving model performance. By employing techniques like polynomial features, log transformations, binning, aggregations, feature encoding, and others, you can create a more informative and effective feature set. The right combination of features tailored to your specific problem can lead to significant improvements in your machine learning models’ accuracy and robustness. Attending Data Scientists Classes that relate advanced techniques like feature engineering will stand machine learning practitioners in good stead in their profession. 

 

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