What are the techniques for identifying irrelevant data in machine learning?

In my recent fetal health classification project, I implemented a data relevance approach to ensure the accuracy of the model. However, challenges emerged during the process. Determining the exact relevance of certain data points posed a significant hurdle, as different stakeholders had varying opinions on what constituted relevant information. Additionally, the availability of comprehensive and high-quality data proved to be a persistent issue, often requiring extensive preprocessing and cleaning. Despite these challenges, my meticulous approach to data relevance ultimately led to a robust and effective classification model, emphasizing the importance of clear problem definition and careful data selection in the realm of machine learning.

Goal oriented data relevance is often overlooked. If a used car site wants to maximize sells via a recommendation model, a feature to EXCLUDE is prior engagement on a car listing! We don't want to always recommend the popular listings to a buyer. If we do so, the other listings are not promoted hence driving overall sales DOWN!

In my "Carvaan Insurance Buyer Analysis" project, I used feature selection to identify the most critical factors that influence caravan insurance premiums while discarding less relevant attributes. This ensures that the machine learning model concentrates on factors such as customer age and customer driving history, which truly impact premiums while ignoring less important details like car color. Feature selection streamlines the model, enhancing its accuracy and interpretability, ultimately resulting in more fair and efficient insurance pricing, improving customer satisfaction, and benefiting the insurance company's bottom line.

Despite all these techniques, I have recently stumbled upon the model bias problem. The reason of this bias was the data collection from different sources and some of those were sparse. This sparsity in one or more sources can be due to data inconsistencies, data unavailability or can also be the actual values as well. Furthermore, limited data unavailability may worsen the situation as well. I made use of the following techniques, which assisted me to remove data inconsistencies, model bias, and skewness; in different ways. The techniques were: 1. Observations binning/bucketing 2. Quantile transformation techniques (in bining or separately) 3. Sparse transformation and then using yeo-johnson for skeweness removal Feel free to contact

Following are few of the methods that we can employ for checking irrelevant data in ML : - Exploratory data analysis (EDA) - Data profiling - Data visualization - Domain relevance - Bad or ambiguous data quality check

When Solving real-world problems, Data plays the role of fuel for ML models :). When I started working in ML and DL domain, I used to think "Careful Development of ML Models All We Need". Now I believe the solution = "Careful Analysis of data" + "Careful Development of ML Models". Long story short, the first step to solve a problem, one should make sure the quality of the data. The quality of data can be assessed from different perspectives. Based on my experience, the key factors to analyze data quality include: >Sufficiency of Data >Data Redundancy >Data Sparsity >Data uncertainty or the presence of unwanted noise

In my recent project, I recognized the integral link between data relevance and data quality, emphasizing their collective impact on the overall performance of the ML model. I faced the challenge of dealing with poor data quality, and issues like missing values, and outliers, which could have led to biases in the model. To address these concerns, I implemented a comprehensive strategy encompassing data visualization for pattern analysis, rigorous validation protocols to ensure data completeness and robust data cleaning techniques such as imputation. This approach not only facilitated the identification and removal of poor-quality data but also played a pivotal role in enhancing the ML model's overall accuracy, reliability, and efficacy.

Data quality is important problem in any ML task, there are different errors: 1. missing data (sometimes usage of mean or median is the worst you can do - mostly in time series) - search for solutions based data logic 2. very noisy data (think about basic math operations - Fourier or Gaussian transformations, means, deviations) 3. extreme values can be gold and dirt at the same time (you need them for you model and they can also destroy it), finding them with normalization is bringing another assumption into process, think alternatively. Beware what data you are removing - you may change the real problem to unreal. Check if quality of training data is the same as online data feeds used in deployment (it can save you a lot of work).

Be cautious about balancing data removal to support model quality, with the loss of valuable information. I have seen data being removed, resulting in a high quality ML model. However this high quality model was for a subset of operations with valid alternative modes of operation omitted. The effort was therefore incomplete and the team then visited the important decision of whether to include alternative modes of operation within this high quality model - and impact its performance - or create a different model.

Since any aspect of data quality would deserve an entire discussion, here I'll focus solely on missing values. To start with, missing values can come in different shapes and forms, and with it I refer to their encoding in the data. In my experience, for instance, they can be empty cells, strings like "Not replied", or nonsensical values like -9 for height: to find them, simply using built-in functions in Python (or alike) may not suffice. Needed steps include reading relevant documentation, checking all the different data types in the columns, and assess the feasible range of the variables. Then, more on the statistical side, one needs to understand their nature, whether they're "completely at random", "at random", or "not at random".

In my experience, I applied various strategies to handle data redundancy effectively. I integrated feature selection algorithms to identify and eliminate irrelevant or less impactful features, which streamlined the dataset and reduced computational complexity. Additionally, I leveraged feature extraction techniques to transform the data into a more compact and meaningful representation, thereby mitigating the effects of multicollinearity. Through careful feature engineering, I restructured certain features to uncover hidden patterns and reduce the dimensionality of the data, facilitating a more efficient and interpretable model.

I've implemented several strategies to address data redundancy, particularly while working with extensive gene data characterized by numerous features. Given the computational demands of handling such data, I strongly advocate the use of diverse feature selection techniques to mitigate these challenges effectively. Feature selection not only assists in optimizes computational resources but also managing overfitting. Some feature selection techniques such as farward feature selection also require lot memory. Then I typically used mutual information, ANOVA kind of feature selection techniques for huge datasets.

During a hackathon working on a project to gauge customer behavior, we realized that a HUGE dataset doesn’t always equate to QUALITY insights. Instead of diving straight into modeling, we paused to visually inspect our data first, often using scatter plots and heatmaps. Surprisingly, some columns showed almost identical trends. Instead of relying only on algorithms, we started with basic visual checks. It’s crazy how patterns emerge when you just look! So, my advice is simple: before diving deep into algorithmic feature selection, spend time visually exploring your data. It can give you early insights and guide your data refinement process in surprising ways

Identifying and eliminating redundant data is crucial in machine learning. Redundancy adds unnecessary complexity and computational burden, leading to issues like overfitting and multicollinearity. This redundancy often stems from irrelevant or correlated features and derived attributes. Utilizing techniques such as feature selection, extraction, and engineering helps in pruning these excesses. By focusing on these methods, you can enhance data quality and relevance, thereby improving your machine learning model's performance and making it easier to interpret.

Redundant data includes duplicate or highly correlated features that provide little additional information. Techniques for identifying data redundancy include: Example: In a dataset for predicting housing prices, if you have both the square footage and the number of bedrooms as features, these might be highly correlated. You can use correlation analysis or variance inflation factor (VIF) to identify and remove one of these redundant features.

Below are the few techniques to overcome the issue of irrelevant data in ML: * Feature selection: Use correlation analysis and feature importance to exclude irrelevant data. * Outlier detection: Apply statistical methods to detect and remove data points that distort the model's performance. * Cross-validation: Employ cross-validation techniques to identify overfitting caused by irrelevant data. * Dimensionality reduction: Utilize methods like PCA to reduce data dimensionality and eliminate irrelevant components. * Domain expertise: Leverage subject matter knowledge to filter out known irrelevant data points based on the problem statement. * Data quality : Ensure the quality of data remains same during training, test and deployment.

Noise in the data is fairly common. To identify which points are noisy, I would start by creating a baseline model with only 5 samples sets at 20%, 40%, 60%, and 80% of the data. Ideally, if the data contains less noise, most models should exhibit reduced errors with increasing training data. Therefore, plotting the error against the percentage should result in an almost straight line with a slope of 1. The closer the slope gets to 0, the noisier the data is

Univariate Analysis: Analyzing each feature's distribution and statistical properties can help identify irrelevant data. Example: When working on a classification problem, if a feature like "customer ID" has the same value for every instance, it adds no discriminative power and can be safely removed. Recursive Feature Elimination: This technique involves iteratively removing the least important features based on a machine learning model's performance. Example: In a spam email classification task, recursive feature elimination may reveal that the sender's IP address doesn't contribute significantly to the model's accuracy, indicating irrelevance.