Building a successful Machine Learning Model

Machine Learning model – The what
Demystifying the hullabaloo around machine learning that has been making all the noise over the last few years is one simple fact – all machine learning models operate on the same basic principle. If we take enough data, feed it into the machine, the machine can analyze it and predict accurate patterns.
These patterns are how everything from recommendations on Spotify and Netflix, “suggested for you” on Amazon, online chess, or even self driving cars make decisions. The opportunities are exciting and the possibilities endless. The link between big data and machine learning is clear – it goes without saying that the more data it gets, the more accurately it can predict patterns.
The brain of this machine learning is the model – which is essentially a function that takes in inputs, performs a certain operations as directed by the algorithm, and decides/predicts/classifies the result.
Machine learning modelling – The How
A model is the most likely representation of a dataset, whose data is not completely available – meaning, situations where there is a probability, ML tries and makes predictions and decisions with insights that people are most likely to make.
Simplifying it further, if an algorithm is the technique or the rule, if the algorithm is “taught” with lots of data, what we have is a model.
While building a machine learning model, it is very important to understand that real world data is not perfect. It is okay and part of the process to be tweaking the model, altering approaches and tools, and the path to a successful, efficient model is filled with trial and error. Teaching a machine to analyse data independently is challenging and before determining the right model, there could be significant experiments. All that said, this should not be confused with a lack of direction, procedure or laxity.
Machine learning modelling – The process
An effective ML model would essentially be modelled on the following steps:

• Setting a goal for the machine: identifying the problem that ML is expected to solve say prediction, classification, analysis etc.
• Access and assess the data:Deciding on the dataset that would be used as input, and what is the anticipated output.
• Pre-process the data: Algorithms cannot differentiate between noise and information in the data, and therefore it is imperative that data be pre-processed and cleaned. To achieve this, a data analysis tool is usually used. Another aspect that has to be paid attention to is the validity of the data – missing values should be replaced by approximations or comparative values.
• Distribute-divide data: The processed data is split into suitable sets of training data and test data.
• Building and training the model: A crucial step, where the divided training data is used to “teach” the algorithm. In this case, the algorithm is given to both the input and result data to help the machine “see” patterns, and “train” it. This includes plotting the confusion matrix, observing how it “behaves” and concluding if that decision making algorithm was the best fit for the given data.
• Improving the model: Once the desired result is achieved, but is not as accurate, it needs to be improved. This is done by complicating the model or simplifying it. Complication involves adding more features to get a better data fit, or simplifying it to exclude features. To create a large or complex model, simpler datasets can also be merged. Using the test data to see what the algorithm predicts and compare this to the actual data. Once taught and adjusted, the model can be validated with the “holdout” dataset set aside during the pre-processing stage. If the output is reliable, the model is good to go.

Machine learning – The Why
It is by now undoubted that, then why should even be a question. If a company has got data, there can be multiple ways to use it to be a driver for its business. It can be something small as a marketing insight, or significant like using it to drive behavioral economics depending on the business objectives, timeframe, and budget.
Machine learning models – The Where next:
The accuracy of a machine learning model is as reliable as its data. If there is not enough data, and decisions are being made on small subsets of data, it could possibly mean a misinterpretation of a trend or a pattern analysis in the wrong direction. Big data is vital in training machine learning models, and enterprises can apply machine learning to as much as their imagination and innovation allows.
With machine learning algorithms being easily available through open-source communities there are huge resources, frameworks, and libraries that have made development easier.
Also, an organisation will not be using machine language in isolation. Used in combination with deep learning, neural networks, AI, IoT and several other techniques, when the model is online, it continuously garners data, and constantly produces results. Leveraging it to create more reliable results is however the key to a model’s success.
Learn more about how we are creating exciting solutions in unconventional arenas, and transforming organisations. Head to https://www.yash.com/digital-transformation/
References:
https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems
https://docs.aws.amazon.com/machine-learning/latest/dg/creating-ml-model-on-the-amazon-ml-console.html
https://www.ibm.com/downloads/cas/GB8ZMQZ3

• Big Data
• Digital Transformation
• Machine Learning