What is a Model in Machine Learning
Author(s): Or Izchak
These classification models can be classified in different ways called Principal Component Analysis, Dimensionality Reductions, Clustering, Regression models, Classification models, etc. Let’s find out more about them.
What is building a model in machine learning?
Building a model in machine learning is creating a mathematical representation by generalizing and learning from training data. Then, the built machine learning model is applied to new data to make predictions and obtain results.
The model you build can be either a regression model or a classification model based on the target variable which is known as the Y variable. If the target variable has a quantitative value, you should build a regression model. If the data type of the target variable is qualitative, you should build a classification model.
Lack of data can be a challenge in building machine learning models. Even if you have access to enough data, they should be in good shape and clean before building the model.
What are the models used in machine learning?
There are two types of machine learning: supervised learning and unsupervised learning.
Supervised Learning:- In supervised learning, machine learning models try to figure out the best dependencies and relationships between the input values and targets. You need to provide a labeled input training dataset for supervised learning. The machine learning algorithms discover patterns from that data set. Then, it builds a model that can be used on new data which is called test data.
Unsupervised Learning — Unsupervised learning creates clusters using input data. In unsupervised learning, you should provide a dataset without output values. So, the machine learning models can figure out patterns, rules, and summaries of similar data points.
Let’s see the different types of supervised and unsupervised learning models.
Types of Supervised Learning Models
Regression Models — You can build a regression model if the output variables of your problem have continuous values. For example, you build regressions models to predict housing prices.
Classification Models — You can build classification models to predict the class or type of an object from a finite number of options. In these models, the output variable is always categorical.
Classification models are of two types. Those are multiclass classification models and binary classification models. Multiclass classification models are used to predict multiple classes (more than two outcomes), while binary classification models generate predictions for a binary outcome (one of two outcomes).
For instance, you should build a multiclass classification model to predict whether a product is clothing, food, or book. Similarly, you should build a binary classification model to predict whether an email is a spam or not.
Types of Unsupervised Learning models
Clustering — You can build a clustering model to group similar items together. It allows you to identify similar items quickly without manual intervention.
Association Rule — You can build an association rule model to find out associations in data. For instance, if you purchase a smartphone, you are more likely to buy a phone case too.
Dimensionality Reduction — You can build a dimensionality reduction model to generalize data and extract meaningful information.
Deep Learning — In deep learning, you use neural networks to build machine learning models. Deep learning models can be classified as Autoencoders, Boltzmann machines, Recurrent Neural Networks, Convolution Neural Networks, and Multi-Layer perceptrons based on their neural network architecture. You can use deep learning models in supervised or unsupervised machine learning.
What is the most important metric in determining the value of a machine learning model?
So now that you know the different types of models available in ML, what metric is most important to determine the value of a machine learning model.
The most important metric of a classification machine learning model is accuracy. Accuracy is the ratio of the total number of correct predictions to the total number of data points in the testing dataset.
Accuracy is a way to assess the performance of a model, but there are other ways. However, accuracy gives the best perspective of the performance of a model for a given dataset compared to other metrics.
We use metrics to evaluate the performance of a model. Next, we will discuss the model parameters the model needs to make predictions.
What is a model parameter in machine learning?
A model parameter is a value that is learned and estimated during training from the dataset. The value should be approximated from the training data. So, model parameters are internal variables of the machine learning model.
Model parameter values are set based on the training. Machine learning models use these values to make predictions. The performance of the model is based on the accuracy of the values of the model parameters.
Model parameter values have some degree of control over the complexity of a model. In the next section, we’ll discuss the complexity curve of a model.
What is a model complexity curve in machine learning?
Model complexity curve is a graphical plot that is used to express the complexity of a model. It shows the relative increase of information when the sample size grows.
The complexity curve can be used for data pruning and it can speed up the learning process without affecting classification accuracy.
The complexity curve allows you to compare the model’s performance on training and validation data as the model becomes more complex or less complex.
In general, more complex models give the best performance compared to simple models. There are many parameters in complex models. Therefore, they can give a good fit for the desired outcome by adjusting these parameters during the training. So, their error rate will be much lower.
In machine learning what is a model artifact?
When you train an ML model, you should provide training data to the machine learning algorithm to learn from. Artifact is the output created from the training process. It could be a file generated by the training process, a model checkpoint, or a fully trained model. For instance, if you train a deep learning model, you gain trained weights as the outputs. So, they are the artifacts.
What is the correct order of steps to take in building a machine learning model?
There are seven steps you need to follow when building machine learning models. You shouldn’t ignore any of these 7 key steps in machine learning model development.
1. Collect Data — First of all, you should collect data. This step is extremely important since the quality and amount of data you collect have a huge impact on the output of your machine learning model.
2. Prepare the Data — You need to shape the collected business data so that they can be used to train a machine learning model. The performance of the machine learning model is based on the quality of data. Preparing the data means the pre-processing of data by removing duplicates, correcting errors, and normalizing. So the data preparation involves data cleansing, transformation, aggregation, normalization, augmentation, and data labeling.
3. Choose the model — When your data is in usable shape, you can choose your machine model. You should select the appropriate machine learning model based on your business objectives. Depending on what target you want to achieve you can select machine learning models including linear regression, deep learning, clustering, classification, and so forth. Choosing the right model for your data can be a hard decision and sometimes require a lot of trial and error of different models, here is a nice map that can help to choose the right model link.
4. Train your model — Training the machine learning model is at the heart of building machine learning models. The machine learning model is going through the learning part in this stage. In this stage, the model is going through retraining until the machine learning model gives the desired level of accuracy.
5. Evaluation — In the evaluation step, you do the quality assurance for your machine learning model. You evaluate your machine learning model using matrix calculations, quality measurements, and model metric approach.
6. Parameter Tuning
After you do the evaluation, you can check whether you can further improve the training. You have to do parameter tuning for that. You can show the full dataset to your model, so it can finetune parameters and improve predictions. You can also show the full dataset several timers to enable the model to make more accurate predictions.
7. Prediction or Inference — After you complete all the previous steps, you can use your machine learning model in real-life scenarios. The final stage of the model-building process is called prediction or inference. This is the model deployment stage where you can use the machine learning model to solve real-world problems. This process involves serving machine learning models in production.
Machine Learning Model Management plays a significant role here as it allows to bring the machine learning model from the development phase to the production level. You can use Azure machine learning for the deployment of model management.
You can use the machine learning pipeline to automate these machine learning workflow. It allows you to correlate and transform the sequence data together in a model, so you can achieve desired outputs. Machine learning pipelines can transform raw data format into useful information which you can use to extract insights.
Selecting the right machine learning model for a specific use case is vital to obtain the desired results. You can define KPIs and evaluation metrics to compare the performance between different machine learning models for your specific business problem. You can choose the best model after checking the statistical performance of each model.
Published via Towards AI