Feature importance analysis helps identify which features have the greatest impact on the target variable that the model is trying to predict. For the household income prediction model, feature importance analysis was done to understand which variables like age, education level, marital status, job type etc. are the strongest predictors of how much income a household is likely to earn.

The specific technique used for feature importance analysis was permutation importance. Permutation importance works by randomly shuffling the values of each feature column across samples and measuring how much the model’s prediction accuracy decreases as a result of shuffling that particular feature. The more the model’s accuracy decreases after a feature is shuffled, the more important that feature is considered to be for the model.

To conduct permutation importance analysis, the pretrained household income prediction model was used. This model was trained using a machine learning algorithm called Extra Trees Regressor on a dataset containing demographic and employment details of over 50,000 households. Features like age, education level, number of children, job type, hours worked per week etc. were used to train the model to predict the annual household income.

The model achieved reasonably good performance with a mean absolute error of around $10,000 on the test set. This validated that the model had indeed learned the relationship between various input features and the target income value.

To analyze feature importance, the model’s predictions were first noted on the original unshuffled test set. Then, for each feature column one by one, the values were randomly shuffled while keeping the target income label intact. For example, the ages of all samples were randomly swapped without changing anyone’s actual age.

The model was then used to make fresh predictions on each shuffled version of the test set. The increase in prediction error after shuffling each feature separately was recorded. Intuitively, features that are really important for the model to make accurate predictions, shuffling them would confuse the model a lot and massively increase the prediction errors. On the other hand, if a feature is not too important, shuffling it may not impact predictions much.

Repeating this process of shuffling and measuring increase in error for each input feature allowed ranking them based on their importance to the underlying income prediction task. Some key findings were:

Education level of the household had the highest feature importance score. Shuffling education levels drastically reduced the model’s performance, indicating it is the single strongest predictor of income.

Job type of the primary earner was the second most important feature. Occupations like doctors, lawyers and managers tend to command higher salaries on average.

Number of hours worked per week by the primary earner was also a highly important predictor of household earnings. Understandably, more hours of work usually translate to more take-home pay.

Age of the primary earner showed moderate importance. Income typically increases with career progression and experience over the years.

Marital status, number of children and home ownership status had lower but still significant importance scores.

Less important features were those like ethnicity, gender which have a weaker direct influence on monetary income levels.

This detailed feature importance analysis provided valuable insights into how different socioeconomic variables combine together to largely determine the overall household finances. It helped understand which levers like education, job, work hours have more power to potentially enhance earnings compared to other factors. Such information can guide focused interventions and policy planning around education/skill development, employment schemes, work-life balance etc. The results were found to be fairly intuitive and align well with general reasoning about income determinants.

The permutation importance technique offered a reliable, model-agnostic way to quantitatively rank the relevance of each feature utilized by the household income prediction model. It helped explain the key drivers behind the model’s decisions and shine a light on relative impact and significance of different input variables. Such interpretable model analysis is crucial for assessing real-world applicability of complex ML systems involving socioeconomic predictions. It fosters accountability and informs impactful actions.