ML Models: A Guide to Feature Selection Methods

ML models are sets of highly performant algorithms that base their “learning” on the datasets that are provided as input. Given that fact, it becomes essential to ensure that the input datasets are relevant, high quality and verified beforehand to enhance the quality of the output results.

This is where ML model selection methods help data scientists vet the quality of their ML models and outputs.

ML model feature relevance is key to ensuring that your ML models learn from relevant, high-quality data and produce results that are accurate and reliable. ML model feature selection is a detailed exercise in itself that incorporates determining the relevance and suitability of input datasets for training ML models.

The Significance of Feature Selection

Feature selection is the process by which data scientists select only the most relevant and high-quality data to be input into an ML model.

It essentially involves removing the noise and fluff from the dataset that is selected and choosing only the most meaningful, appropriate values that train your ML model for the outputs that it is expected to deliver.

To put it simply, the input variables that are fed into a machine-learning model are called features. You can consider each column in the dataset as a feature that influences how your ML model learns and functions.

As such, only the most relevant features can train an ML model for accuracy, speed, and reliability of output.

The reduction in the number of input variables streamlines the computational costs of modeling a machine learning system.

Irrelevant features can severely impact the learning process of your ML models, leading to overfitting. When a model is loaded with irrelevant data, it leads to over-interpretation of patterns in the input data, which ultimately impacts the prediction quality of the model.

The predictive performance is altered by the presence of new data as the algorithms deal with overload.

One of the solutions to this issue is to finetune the data that goes into the model by applying simple, statistical (or other) techniques for feature selection.

ML Model Selection Methods

Discussed below are four of the most effective methods of feature selection to train ML models for high-impact and accurate predictions:

1. Filter Methods

The filter method is a supervised model for ML model feature selection. It is a statistical method that data scientists use to determine the extent of correlation of the input data to the output value.

The intrinsic properties of input features are measured using univariate statistics to determine whether there is a positive or negative correlation with the output.

2. Wrapper Methods

The wrapper method is iterative in nature and involves splitting a dataset into subsets in order to train ML models. The output quality of the model is then gauged to adjust the features in the subsets accordingly for inputting into the second round of iteration for finetuning.

This method is useful for determining the accuracy of all possible permutations and combinations of the features but is computationally extensive.

3. Embedded Methods

Embedded methods are a combination of filter and wrapper methods. They use algorithms to automatically select features and to track the relevant features using certain or specified criteria.

It is computationally less expensive than the wrapper method and can perform feature selection in ML model training in tandem.

4. The Role of Dimensionality Reduction

The dimensionality reduction method involves working with the complexity of the input features. By reducing the number of features, this model reduces the complexity of the dataset while preserving the relevant attributes of the original data.

This method also reduces the storage space required for the dataset and improves the performance.

Challenges in Feature Selection

The following ML Model Feature selection challenges need to be streamlined in order to achieve ML models that are trained for high accuracy and performance:

1. Curse of Dimensionality

The curse of dimensionality refers to the situation where the storage available for features becomes increasingly sparse for an increasing number of dimensions in a fixed-size training dataset.

The higher-dimension data becomes challenging to work with in low-dimension settings. You can employ dimensionality reduction to improve the closeness of data to provide accurate predictions using your ML model.

2. Handling Missing Data

Incomplete datasets that have missing values in them can negatively impact the prediction outcomes of your ML models. The best way to work around this challenge is to delete the rows or columns that have null values.

Other ways include using imputation and modeling to populate the missing values.

3. Ensuring Model Robustness

The robustness of your ML model depends on the quality of data that is fed to it for training. Real-world data is hardly ideal – it is inconsistent, erroneous, and may contain missing values.

The challenge is to ensure that only the most relevant and high-quality datasets are used to train your model, which in turn is what ensures its robustness.

Strategies for Effective Feature Selection

1. Data Exploration

Data exploration enables data scientists to identify outliers in a dataset, helping select only the most relevant features for the ML model.

One example of data exploration is to sort numerical values according to a certain order into a table, histograms, charts, or graphs to identify patterns and discrepancies.

2. Cross-Validation

The cross-validation technique helps data scientists to test the performance of an ML model on unseen data. It is an excellent method to use for feature selection to optimize the hyper-parameters that go into training an ML model.

For example, one subset of a complete dataset can be set aside for resampling in cross-validation.

3. Model-Specific Feature Selection

ML models are not general-purpose – they are designed to perform specific functions. Depending on the intended purpose of your ML model, feature selection can be simplified by applying relevant filters and checks to the dataset.

For example, training an inventory prediction ML model in eCommerce requires historical data pertaining to sales and channel traffic above anything else.

4. Interpretability and Transparency

To gauge the transparency of your ML model, it is essential to observe the model’s weights and input features that determine the output. On the other hand, interoperability leverages statistical methods to enhance visibility inside the black box of the ML algorithms.

For example, an ML model used for predicting the likelihood of cancer can be made to display its computations to help doctors understand how it reached that prediction.

Conclusion

Feature selection is a key exercise that determines the accuracy of output and efficiency of computation in ML models. Where irrelevant data works to introduce noise and distractions into predictions, careful feature selection keeps an ML model well-oriented with its purpose and function.

No-code AI platforms, such as MarkovML, enable knowledge workers to gain data insights efficiently with just a click of a button. Within minutes, these workers can gain meaningful insights into their data and perform relevant tasks to obtain the required outputs.

Schedule a demo with Markov today to understand the full width of the platform and its capabilities.

FAQs

What is data exploration?

It is the first step of data analysis that involves sorting through the datasets to identify relevant inputs, eliminate noise, and generate visualizations for identifying patterns and trends.

What are the benefits of feature selection?

Feature selection improves the accuracy of the predictions that ML models generate. It also helps reduce the time it takes to generate predictions. Feature selection also helps reduce overfitting in the ML models.