Please enable JavaScript.
Coggle requires JavaScript to display documents.
ML Models, Deep Learning Model, Regression: a statistical method that…
ML Models
What is ML Model
- A program that can find patterns or make decisions from a previously unseen dataset
- i.e., NLP (Natural Language Processing): ML model can parse and correctly recognize the intent behind previously unheard sentences or combinations of words
- in Iimage recognition, a ML model can be taught to recognize objects - such as cars or dogs.
- A ML model can perform such tasks by having it 'trained' with a large dataset. During training, the ML algorithm is optimized to find certain patterns or outputs from the dataset, depending on tasks.
- The output of this process - often a computer program with specific rules and data structures - is called a machine learning model
Machine learning Algorithm
- A mathematical method to find patterns in a set of data
- drawn from:
- statistics,
- calculus,
- and linear algebra
- popular examples of machine learning algorithms include:
- linear regression,
- decision trees,
- random forest,
- and XGBoost
What is Model Training in ML
- The process of running ML algorithm on training set(dataset) and optimizing the algorithm to find certain patterns or outputs
- The resulting function with rules and data structures is called the trained machine learning model.
The different types of ML
- supervised learning
- unsupervised learning
- reinforcement learning
supervised ML
- The algorithm is provided an input dataset, and is rewarded or optimized to meet a set of specific outputs
- widely deployed in image recognition, utilizing a technique called classificatin
- also used in predicting demographics such as population growth or health metrics, utilizing a technique caleed regression
-
SVM (Support Vector Machines) : create coordinates for each object in an n-dimensional space and uses a hyperplane to group objects by common features
Naive Bayes: assume independence among variables and uses probability to classify objects based on features
Decision Trees: also classifiers to determine what category an input falls into by traversing the leaf's and nodes of a tree
- A predictive approach in ML to determine what class an object belongs to.
- A tree-like flow chart where the class of an object is determined step-by-step using certain known conditions
Linear Regression: used to identify relationships btw the variable of interest and the inputs, and predict its values based on the values of the input variables
kNN (the k Nearest Neighbors): involves grouping the closest objects in a dataset and finding the most frequent or average characteristics among the objects
Random Forest: a collection of many decision trees from random subsets of the data, resulting in a combination of trees that may be more accurate in prediction than a single decision tree
Boosting algorithms: such as Gradient Boosting Machine, XGBoost, and LightGBM, use ensemble learning. Combine the predictions from multiple algorithms (i.e. decision tree) while taking into account the error from the previous algorithm.
unsupervised ML
- The algorithm provide an input dataset but not rewarded or optimized to specific outputs, and instead trained to group objects by common characteristics
- recommendation engines on online stores rely on unsupervised ML, specifically a technique called clustering
-
Hierarchical Clustering: builds a tree of nested clusters without having to specify the number of clusters
reinforcement learning
- The algorithm is made to train itself using many trial and error experiments.
- Reinforcement learning happens when the algorithm interacts continually with the environment, rather than relying on training data.
- One of most popular examples: autonomous driving
Model deployment:
- The process of making a ML model available for use on a target environment - for testing or production
- Usually integrated with other applications and environment (i.e. DB and UI) through APIs.
- The stage after which an organization can actually make a return on the heavy investment made in model development
Deep Learning Model
- A class of ML models that imitate the way humans process information.
- Consists of several layers of processing (hence the term 'deep') to extract high-level features from the data provided.
- Each processing layer passes on a more abstract representation of the data to the next layer, with the final layer providing a more human-like insight.
- Unlike traditional ML models which require data to be labeled, Deep Learning models can ingest large amounts of unstructured data.
- They are used to perform more human-like functions such as facial recognition and (NLP) natural language process
Regression: a statistical method that enables predicting outcomes based on a set of input variables.
- The outcome is often a variable that depends on a combination of the input variables.
Classifier:
- An algorithm that assigns an object as a member of a category or group
- i.e. classifiers are used to detect if an email is spam, or if a transaction is fraudulent