What is Model Training??
Model training is the process of training a model to make a prediction or perform a task by exposing the model to a set of data. The model is trained based on the model’s parameters, the data it is trained on and the correct output it produces (labels). The goal is to reduce the difference between predicted and actual output.?
Elements use to Train Machine Learning Model?
Parameters—Model parameters are the values that the machine learning model learns from the input data during the training phase. For instance, in neural networks, model parameters are neuron weights. For logistic regression models, model parameters are regression coefficients.?
Hyperparameters—Hyperparameter values are defined outside of the machine learning model and determine how it works. Different hyperparameter values can lead to different performance levels for the same machine learning model and dataset. This is why hyperparameter tuning is so important. While tuning hyperparameters manually is the most common approach, there are several ways to automatically search through hyper parameter values to find the best settings.?
Inputs—It is essential to specify the inputs that the model will get, the format of those inputs, and the form in which the data is presented. For instance, a computer vision model can handle images of a specific size, quality, color, or black-and-white, and it may be required that the images are vectorized before the model can process them.?
Top Machine Learning Tools for Model Training?
- TensorFlow: TensorFlow is an ML library and requires close integration with the model code, developers can gain complete control and build models from the ground up using TensorFlow. TensorFlow also comes with a few pre-built models for easier solutions. One of TensorFlow’s most popular features is dataflow graphs, which are especially useful when you’re working on complex models.?
- PyTorch: The PyTorch framework is a fully featured deep learning model builder. Deep learning is a subset of machine learning, which is widely used in applications such as image recognition, language processing, etc. It is written in Python and is relatively easy to learn and deploy for most machine learning developers. What sets PyTorch apart from other deep learning frameworks is its great support for GPUs, as well as its use of auto-differentiating compute graphs using reverse-mode. This allows you to modify compute graphs in real-time, which is why PyTorch has become one of the go-to frameworks for rapid testing and prototyping.?
- Sci-Kit Learn: One of the best open-source frameworks for learning machine learning is Scikit-learn. With its high-level wrapper, Scikit-learn allows you to play around with many different algorithms and explore the vast array of classification, cluster and regression models.?
- XGBoost: XGBoost model training algorithm is a tree-based model training algorithm with gradient boosting. XGBoost is an ensemble learning algorithm which means several tree-based algorithms are used to get the best model sequence.?
- LightGBM: LightGBM is also a tree-based gradient boosting algorithm, similar to the one mentioned above. However, the main difference between the two is the speed of LightGBM, which is much faster than XGBoost. LightGBM is well-suited for large datasets, which would otherwise require a significant amount of training time for other models.?
How to train Machine Learning Model??
- Split the dataset: Your raw training data is a finite resource that must be allocated wisely. Some of the data can be used for training your model, and some can be used for testing your model – but it can’t be used for both at the same time. You can’t push a model if you haven’t given it a new set of data that it’s never seen before. Splitting your training data into two or more groups lets you train and test your model using a single data set. This enables you to see if your model is overfit – that is, it works well with your training data but not so well with your test data.?
- Select Algorithm to test: There are thousands of algorithms in machine learning, and there’s no one-size-fits-all answer to “which algorithm will work best for any particular model.” In most instances, you’ll run dozens if not hundreds of algorithms before you find the one that produces a working model.?
- Tune the Hyperparameters: Hyperparameters, on the other hand, are the top-level parameters defined by your data science team prior to building and training your model. Many attributes can be extracted from training data, but they can’t learn on their own.?
- Fit and Tune Models: Once the data is ready and the model's hyperparameter values have been calculated, it's time to train the models. This involves looping through the various algorithms using each hyperparameter value you've chosen to investigate.?
?Conclusion?
In conclusion, the use of machine learning in model training has revolutionized the way we approach and solve complex problems across various domains. The adoption of machine learning techniques has significantly enhanced our ability to analyze large datasets, the process of identifying trends and making predictions or making decisions based on insights derived from data.?
The integration of machine learning in model training has reshaped the landscape of data analysis and decision-making. The ongoing advancements in machine learning algorithms, coupled with increased computing power and access to large datasets, continue to push the boundaries of what is achievable. As we navigate the future of technology, the responsible development and application of machine learning will be pivotal in unlocking its full potential for the benefit of society.?
