3 Machine Learning Algorithms That Can Be Applied to Big Data
Big data involves working with huge chunks of structured and unstructured data. The sheer volume of data most data scientists must work on often exceeds over millions of rows making it tedious to prepare. With machine learning and artificial intelligence, a data scientist can process big data in ways never done before. If you consider the volume of datasets, software models and conventional databases turn out to be less effective. This is exactly why you should seek to leverage the power of machine learning algorithms that can be applied to big data.
There are 3 types of algorithms in machine learning that can be used for big data classification: Supervised, Semi-supervised and Unsupervised. Let’s define what they are and why they’re important!
Some of the most commonly used supervised learning algorithms include Support Vector Machines (SVM) and Naïve Bayes. In fact, the majority of practical machine learning uses supervised learning.
It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher closely monitoring the learning process. Since we (the teacher) know the correct answers, the algorithm iteratively makes predictions on the training data and improves as it is corrected by said teacher. You’re probably wondering, “Does the learning ever stop?” Yes! Learning stops when the algorithm achieves an acceptable level of performance.
Semi-supervised learning problems are those that sit between both supervised and unsupervised learning. Essentially, these are problems where you have a large amount of input data (X) and only some of the data is labeled (Y). This one can be a little tricky so we’ll spend longer on it.
You have probably encountered an example of this data on Facebook. Have you ever uploaded a picture in which your face and a friend’s face have been identified and labeled, but the majority of the image is not? This is the type of data often found in semi-supervised learning problems. As previously stated, semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. This is useful for a many reasons. To begin, the process of labeling massive amounts of data for supervised learning is incredibly time-consuming and can be quite expensive. Moreover, too much labeling can impose human biases on the model so that is something you should seek to avoid. The good news is that including lots of unlabeled data during the training process tends to improve the accuracy of the final model while reducing the time and cost spent building it.
In unsupervised learning, algorithms tend to take unlabelled data and classify it by drawing a comparison among data features. In other words, unsupervised learning is where you only have input data (X) and no corresponding output variables. So what’s the goal? The objective for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.
In short, machine learning algorithms help people structure and learn from data rather than be overwhelmed by it. It has a wide range of implications and benefits because you can train a system to learn from data, identify patterns and make decisions with minimal human intervention. This frees people up to spend more time on value-add tasks that require human touch and avoid spending time and money on time-intensive, iterative work. Wish to learn how Advoqt can help your company apply machine learning algorithms to data classification and exploratory analysis? Contact us today!
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