Supervised vs Unsupervised Machine Learning
Supervised Machine Learning
In supervised machine learning we feed the model a dataset that we have the asnwers to. In othe words, we have labels that are associated with the training dataset which are used to correct the algorithm.
The clean dataset will be fed to a model for processing and eventually the model will make some prediction based on the pattern it finds in the attributes of that dataset.
Our dataset consists of rows and columns. Row are called Observations and columns are called attributes.
Unsupervised Machine Learning
In unsupervied Machine Learning there is no existing dataset labels. Basically in unsupervised ML, there is no training dataset and the outcomes are unknown. The model goes into the problem blindly. For example in case of detecting images of humans vs cats.
Contrary to supervised learning, in unsupervised learning the model has to be setup right to learn structure in the data. In this case there are no labels that can be used to correct the model instead the model has to be setup just right to learn the patterns or structure.
Unsupervised learning does not have
y variables and they don’t work off of
labeled corpus and hence there are no explicit training phase.
Unsupervised ML algorithms are also used for
Dimentionality reduction of the input dataset.
Here we try to find the significant variables that drive the input data. For example in
Principal Component Analysis is a pre-training step for other supervised learning techniques such as classfications or regression.
Machine Learning Process
- Collect data
- Cleanse data
- Model building. Modeling involves feeding data to mathematical equations.
- Prediction: Once the model has been tuned and tested, it is ready for use. The idea is that the model should perform well on datasets that has never seen.
Supervised Learning Tasks
Most common type of applied machine learning is classification
When data is used to predict a category, supervised learning is also called classification.
This model usually has two ML-based classifiers:
- Training In training phase we feed the model a large dataset which the model will use for training. This dataset has been classified correctly so that the model can learn from.
- Predication Once the model has been trained, we run it in the prediction phase.
In this phase we try to classify new instances which have not been seen before.
When a value is being predicted, like with stock prices, supervised learning is called regression. This is usefull to finding relationships between two continues variables.i.e. Predicting housing prices or stock prices are examples of linear regression. The idea is figure out a line that best fits the data.
Unsupervised Learning Tasks
In this technique, the idea is to group data points. Given a set of datapoints we can use algorithem to classify each data point into a specific group.
In theory, datapoint within a same group should have similar properties.
One of the main techniques is to identify patterns in data items e.g. K-means clustering.
In association we look for combination of items that occur together frequently in transactions (mainly used in large dbs or datasets). The goal is to find associations of items that occur together more often than you would expect in a random sample. The famous example is the beer and diaper association.
Used for building ML models. It is not used for DL models rather for traditioanl models. After data wrangling and model selection there are 3 things you need to do with every model.
1. Fit the model
Training part of data modeling process
2. Predict method
Classify incomfing data points here we make prediction base on the patterns found in the data.
determine the accuracy of the model.
Data Types used in ML
There are 2 broad types of data that ML algorithms work with:
- Continuous data can take on a inifinite set of values (hight, weight, income, ….)
- Categorical data
Can only take on a finite set of values (Day of the week, month of the year, …)Categorical variables that take on just 2 values are called
binary variables. (0 or 1, On or Off, Light or Dark, Fraud or Not, etc…)