A Brief About AutoML
Automated machine learning basically involves choosing the model algorithm, optimising the hyperparameters, modelling iteratively and evaluating the model. Instead of trying to replace data scientists, this technology hopes to relieve them of tedious work. Parts of the data science workflow are being automated using AutoML in an effort to promote data-driven decision making. AutoML aims […]
Outliers And Their Treatment
How Does a Data Scientist Interpret Data?
Ever since the beginning of human civilisation, data has been produced. But it hasn’t been until recently that we’ve been able to unlock its full potential and learn from it. We have only recently begun to think of data as an industry’s fuel. The increase in computer capacity is the fundamental driver of this most […]
Data Mining and Machine Learning
Exploring the available datasets to find patterns and anomalies is known as data mining. The technique of learning from heterogeneous data in a way that may predict or forecast unknown / future values is known as machine learning. Together, these two ideas make it possible to depict historical data and anticipate future data. Data Mining […]
Underfitting and Overfitting in Machine Learning
The two main issues that affect machine learning and lower the effectiveness of the machine learning models are overfitting and underfitting. A model is considered to be a good machine learning model if it appropriately generalises any new input data from the problem area. This helps us to make predictions about future data, that the data model has […]
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