Machine Learning models are programs trained to find patterns in new data to make predictions. They are essentially a mathematical function that accepts requests as input data, makes predictions on it and gives an output in response to that.
When building machine learning models, they are first trained on a set of data to perfect the process.
Six steps to building machine learning models
Here are the six steps involved in building machine learning models.
1. Collecting data
The first step is to collect data regarding the problem you aim to solve with the machine learning model. Collecting good quality data is essential as the quality of collected data directly impacts the quality of the model. If the data collected is inadequate or of poor quality, then the model can turn out to work badly. You can either use information from a pre-existing database or create it from scratch.
2. Preparing the data
The next step is to visualize the collected data and check for correlations between different characteristics. It is important to select characteristics carefully since they directly impact execution times and results. The amount of data should also be balanced for each result. You can separate the data into two groups – training and model evaluation approximately in the ratio of 80:20 depending on the volume of data. Other processes like removing duplicates and correcting errors can also be done at this stage.
3. Choosing the model
You can choose from a variety of models depending on your objective, and the type of data you want to process such as text, numerical data, images or sounds. You can use algorithms for classification, clustering, prediction, and linear regression.
4. Training the machine model
The next step is to train the datasets to increase their efficiency and precision. Training helps them run smoothly and improves their prediction rate. You should randomly initialize model weights, the values affecting the relation between input and output.
5. Evaluating
Check the created machine against evaluation data sets that have inputs unknown to the model to verify the precision of the model you trained. The accuracy needs to be equal to or more than 50% for the model to be useful. The best model is the one with 90% or more accuracy.
6. Tuning the parameter
If you don’t get good or minimum desirable predictions, you may have underfitting or overfitting problems that can be fixed by returning to the training step before making a new configuration of parameters in the model.
The learning rate is another important parameter which is a value that multiplies the gradient and brings it closer to the minimum to reduce the cost of the function.
Hyperparameters help you indicate the maximum error allowed for your model. The tuning doesn’t have a set duration and can take a few minutes to hours to even days till you perfect the model.