5 ways to make your data work harder with Machine Learning
It is a known fact that Machine Learning mainly depends on data. The data you offer to ML decides what results in you get. Flawed datasets and improper data collection mechanisms are two prominent reasons behind the inaccuracy and inefficiency of Machine Learning processes. These largely hamper the advantages that Machine Learning has to offer.
It is a known fact that Machine Learning mainly depends on data. The data you offer to ML decides what results in you get. Flawed datasets and improper data collection mechanisms are two prominent reasons behind the inaccuracy and inefficiency of Machine Learning processes. These largely hamper the advantages that Machine Learning has to offer. The Global Machine Learning Market is expected to expand at 42.08% CAGR during 2018–2024.
To achieve the best possible results, your data should be accurate and reliable. To make the most out of Machine Learning, you need to know the following 5 ways to make your data work harder:
- Formulate the problem properly
Knowing what you want to achieve out of ML helps you figure out which data you need to collect. Articulating the problem early and properly ensures that you do not miss out on any significant metric or component that directly affects end results.
To collect the right data, it is important to keep the following categories in mind:
Think if you need the end result in the form of binary (yes or no), or you have to get multiple answers. To make the algorithm learn, it is crucial to label answers rightly.
When you want your algorithm to offer a numeric result, regression has a great value. Using regression algorithms, you can make sure you counted in all the factors that can affect data.
Many ML algorithms use a ranking system. Using them when you need rank-wise classification can be helpful.
- Create the right data-collection mechanism
This is the hardest thing to do but can make your data work hard with ML. Creating the right data-collection mechanism is important because it defines what data you are going to collect and how. When data is collected accurately, it is inferred by algorithm properly.
- Choose the right formatting
One reason why data fails to get along with the algorithm is due to the wrong formatting. When both data and algorithms use the same format, things can never go wrong. So before you implement the ML algorithm, make sure your data is in the same format as is your Machine Learning system.
This can also be called as data consistency. Usually, datasets coming from different sources are not consistent in their format. Having them consistent ensures your inputs are the same, and not complicated.
- Implement data normalization procedures
By implementing data normalization procedures such as data rescaling, you can highly improve the quality of the dataset. Data normalization ensures that all the useless data is removed, missing data is completed, and the algorithm avoids complexities.
Two major data normalization approaches include min-max normalization and decimal scaling.
Through min-max normalization, you can set extremes of the values. This eliminates data that goes out of the two extremes. It normalizes the entire data set and gets targeted results.
Similarly, decimal scaling is used for data scaling in which decimals are rounded off in either direction towards the same target.
However, these techniques usually come handy with numeric data to make it more accurate yet comprehensive.
- Discretization of data
Another way to make data work harder with machine Learning is the discretization of data. This accounts for categorical values instead of numeric ones. Putting close and relevant figures under the same group can avoid complex results and offer more practical solutions.
For instance, placing close ages such as 18 and 19 under the same group can make data more predictable.
To bring data in its best form is the key to making the most out of ML algorithms. It is important to make sure that data is by the ML system and does not face issues like the wrong data-collection mechanism and over-extensive data etc.
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