Artificial Intelligence (AI) has garnered attention from different fields, owing to its potential for turning vast amounts of data into actionable knowledge. One critical aspect of AI is discriminative learning models that focus on distinguishing or classifying different patterns within data.

To build effective discriminative models, the initial step of data understanding, commonly referred to as Exploratory Data Analysis (EDA), is vital. Findability Sciences has developed an AI powered, fully automatic EDA tool which saves lot of time and efforts. Thought of sharing my thoughts on this important step in discriminative AI implementation

So what is Exploratory Data Analysis (EDA)?

EDA is the preliminary step in the data analysis process, where various techniques are applied to understand the nature of the data and to uncover patterns, anomalies, and relationships within it. It involves visual methods and statistical techniques to summarize the data’s essential characteristics. This process is vital in identifying which models would work best on the data set and preparing it for these models.

For most EDA is primarily manual process, requiring the analyst to make certain decisions, there are also automated solutions. Regardless, the goal remains the same: to understand the data’s underlying structure and extract valuable insights that can guide the subsequent stages of the data analysis pipeline. At Findability Sciences we automated this process and our EDA have come down from days to hours!

There may be a question as to what is the importance of EDA in Discriminative AI

Discriminative AI models are focused on the direct estimation of the conditional probability of the output given the input. They are particularly adept at classifying and predicting outputs based on complex relationships between inputs. Examples include Logistic Regression, Support Vector Machines, and Neural Networks. Here are reasons why EDA is pivotal in the implementation of discriminative AI: