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Organic Data: The Key to a Winning Data Science Model
Organic Data: The Key to a Winning Data Science Model

September 14, 2022

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Twitter continuously creates tweet data, traffic cameras digitally count cars, and websites record and store mouse clicks thanks to Internet search engines compiling data sets with each entry. Our digital society gathers enormous amounts of data and self-evaluates in ever-wider spheres. You may determine how many visits come from searches made on search engines using a metric called organic traffic. Text data is one of the unstructured data's most essential and rapidly expanding categories. The practice of analyzing unstructured and semi-structured text data for insightful observations, patterns, and trends is known as text analysis.

 

The necessity for an extensive training data set to create reliable models is one of the main hurdles when working with text data. The training data must be organic, which means it must be abundant, robust, and reliable. An expert data science professional is ideal for resolving complex business problems.

 

Here are five reasons why you should use extreme caution while gathering training data for supervised machine learning models:

 

  • Consistency in Subjectivity

You may come upon a subjectivity issue over how different users interpret a particular text in several situations. An instance of a credit-related sentiment analysis where it may be difficult to distinguish between a negative and positive sentiment in an earnings call transcript. The reliability and consistency of labeling subjective language can be checked with the assistance of a training data overlap analysis. The coexistence of contradictory ground truth values for comparable texts, which can cause confusion in ML models, is prevented by maintaining consistency in the training data.

  • Apply an Unbiased Approach

You need to gather training samples to train the data before you can begin to develop a new supervised machine learning model that involves categorizing and detecting fresh text input. The pattern previously used to search for data is carried over into the data that is thus collected utilizing pre-existing search bars or data queries and employed keywords. This introduces bias into the training of the supervised model. The final model won't be as robust as if trained on completely randomized data because it will strongly rely on the used keywords and other co-occurring solid phrases.

  • Random Data is the Key

Building a randomized data set is essential for creating a powerful model. This lessens the workload associated with gathering training data and gives guidance for creating an organic training data collection. By eliminating the requirement to use search bars to find data, the team is able to move on to the next stage of searching through a spreadsheet to label the randomized data properly. The various iterations of text data randomization and labeling used in this collaborative, iterative process produce clarity and increase insight.

  • Early Error Detection 

The time spent on the measures above helps better comprehend the data and saves time during the model-building process' later stages. Starting the model training process without paying attention to minute but crucial aspects in the training data may result in bias or variance errors and poor model performance. This would eventually result in spending too much time later on adjusting the model or, in the worst situations, shelving the project due to the model performing below expectations. A qualified data science professional with the top data scientist certification can help avoid this significant obstacle by applying specialist expertise to the model's early development stages.

  • Stringent Data Management

Any change in the team composition, any modification of the label definitions as the model develops, or any change in the project scope can significantly influence large data science projects with longer development times. The training data gathered on the project's first day may be completely different from that which was gathered on day fifty. The original training data's quality is impacted, and the model is also subject to systematic disturbance.

 

As we can see from the parameters above, robust modeling requires homogeneous training data. Throughout the model-building process, strict training in data management is required to limit and balance the effect of various stakeholders. The answer is unambiguously in favor of using natural training data to create more robust models. You can create a good ML model if you keep the advice mentioned above in mind.

 

 


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