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Binary vs. Multi-Class ClassificationClassification problems are common in machine learning. In most cases, developers prefer using a supervised machine-learning approach to predict class tables for a given dataset. Unlike regression, classification involves designing the classifier model and training it to input and categorize the test dataset. For that, you can divide the dataset into either binary or multi-class modules.As the name suggests, binary classification involves solving a problem with only two class labels. This makes it easy to filter the data, apply classification algorithms, and train the model to predict outcomes. On the other hand, multi-class classification is applicable when there are more than two class labels in the input train data. The technique enables developers to categorize the test data into multiple binary class labels.That said, while binary classification requires only one classifier model, the one used in the multi-class approach depends on the classification technique. Below are the two models of the multi-class classification algorithm.One-Vs-Rest Classification Model for Multi-Class ClassificationAlso known as one-vs-all, the one-vs-rest model is a defined heuristic method that leverages a binary classification algorithm for multi-class classifications. The technique involves splitting a multi-class dataset into multiple sets of binary problems. Following this, a binary classifier is trained to handle each binary classification model with the most confident one making predictions.For instance, with a multi-class classification problem with red, green, and blue datasets, binary classification can be categorized as follows:Problem one: red vs. green/blueProblem two: blue vs. green/redProblem three: green vs. blue/redThe only challenge of using this model is that you should create a model for every class. The three classes require three models from the above datasets, which can be challenging for large sets of data with million rows, slow models, such as neural networks and datasets with a significant number of classes.The one-vs-rest approach requires individual models to prognosticate the probability-like score. The class index with the largest score is then used to predict a class. As such, it is commonly used for classification algorithms that can naturally predict scores or numerical class membership such as perceptron and logistic regression.

The key actions in the data collection phase include:Label: Labeled data is the raw data that was processed by adding one or more meaningful tags so that a model can learn from it. It will take some work to label it if such information is missing (manually or automatically).Ingest and Aggregate: Incorporating and combining data from many data sources is part of data collection in AI.Data collectionCollecting data for training the ML model is the basic step in the machine learning pipeline. The predictions made by ML systems can only be as good as the data on which they have been trained. Following are some of the problems that can arise in data collection:Inaccurate data. The collected data could be unrelated to the problem statement.Missing data. Sub-data could be missing. That could take the form of empty values in columns or missing images for some class of prediction.Data imbalance. Some classes or categories in the data may have a disproportionately high or low number of corresponding samples. As a result, they risk being under-represented in the model.Data bias. Depending on how the data, subjects and labels themselves are chosen, the model could propagate inherent biases on gender, politics, age or region, for example. Data bias is difficult to detect and remove.Several techniques can be applied to address those problems:Pre-cleaned, freely available datasets. If the problem statement (for example, image classification, object recognition) aligns with a clean, pre-existing, properly formulated dataset, then take ad-vantage of existing, open-source expertise.Web crawling and scraping. Automated tools, bots and headless browsers can crawl and scrape websites for data.Private data. ML engineers can create their own data. This is helpful when the amount of data required to train the model is small and the problem statement is too specific to generalize over an open-source dataset.Custom data. Agencies can create or crowdsource the data for a fee.

Type of visualization used for exploration:Correlation heatmapClass distributions by featureTwo-Dimensional density plots.All the visualizations are interactive, as is standard for Plotly.For More details, please refer the below link:https://towardsdatascience.com/data-exploration-understanding-and-visualization-72657f5eac41