Machine Learning and ML Model?

ML is the process of training a Machine Learning model, to make useful predictions or generate content (like text, images, audio, or video) from data.

Types of Machine Learning

	Supervised Learning	Unsupervised Learning	Semisupervised Learning	Reinforcement learning	Generative AI
What	Training data feed to the algorithm includes the desired solutions(called labels). Example: This is like a student learning new material by studying old exams that contain both questions and answers	Dataset does not have labels. ML model tries to learn without teacher. ML has to estimate whether it's correct/incorrect and create its own rules	lot of unlabeled data and a little bit of labeled data. Examples: 1. FB Photos: When we load photos, we provide labels to few and leave others. AI identifies photos.	Agent(AI Program) can observe the environment, select and perform actions, and get rewards or penalties based on actions performed within an environment Example: used to train robots to perform tasks, like walking around a room	Models that creates content from user input. Eg: Create unique images, music compositions, jokes, Text-to-text Text-to-image Text-to-video Text-to-code Text-to-speech Image and text-to-image
Types	1. Classification (give yes/no): Example   a. Spam Filterning: Algo is trained with many example emails along with their class (spam or ham). Each email has a label. Types of classification models:     1. Binary classification: Model output a value from a class that contains only two values, for example rain or no rain     2. Multiclass classification: Output a value from a class that contains more than 2 values. Eg: rain, hail, snow, or sleet 2. Regression: (give % or numeric value): Predict whether based on inputs, Predict price of car provided with some inputs(mileage, age, brand, etc.) 3. Logistic Regression (yes/no with %): Mix of classfication & Regression. Example: 20% of chances being a spam.	1. Clustering: The model finds data points that demarcate natural groupings. Algorithms used:   k-Means   Hierarchical Cluster Analysis (HCA)   Expectation Maximization 2. Visualization and dimensionality reduction With unlabelled data this algorithm provides ouput which can be plotted on 2-D, 3-D plane. Algorithms used:   Principal Component Analysis (PCA)   Kernel PCA   Locally-Linear Embedding (LLE)   t-distributed Stochastic Neighbor Embedding (t-SNE) 3. Association rule learning: This provides output as relations between attributes. Algorithms used:   Apriori   Eclat
Algorithms	k-Nearest Neighbors Linear Regression Logistic Regression Support Vector Machines (SVMs) Decision Trees & Random Forests Neural networks	Clustering: k-Means, Hierarchical Cluster Analysis (HCA), Expectation Maximization Visualization and dimensionality reduction: Principal Component Analysis (PCA), Kernel PCA, Locally-Linear Embedding (LLE), t-distributed Stochastic Neighbor Embedding (t-SNE) Association rule learning: Apriori, Eclat	Deep belief networks (DBNs)

ML Models

Machine Learning model is a Algorithm, which makes predictions after getting trained on data
Supervised Learning:
  Regression: Decision Tree Regressor
  Classification: Decision Tree Classifier

Supervised Learning Models (Regression)

Decision Tree Regressor

This is Binary Tree(not balanced) which works in flowchart-like manner where each internal node represents a "test" on an attribute, each branch represents the outcome of the test, and each leaf node represents numerical value (ie Prediction).
How it works? Predicting Whether a Customer Will Buy a Product Using Two Decision Trees

If Tree 1 predicts "Purchase" and Tree 2 predicts "No Purchase", the final prediction might be "Purchase" or "No Purchase" depending on the weight or confidence assigned to each tree.

Information Gain in Decision Tree
  Information Gain tells us how useful a question (or feature) is for splitting data into groups
  It measures how much the uncertainty decreases after the split.
  For example if we split a dataset of people into "Young" and "Old" based on age and all young people bought the product while all old people did not, the Information Gain would be high
Gini Index in Decision Tree: Metric to measure how often a randomly chosen element would be incorrectly identified. Lower Gini index represents a better split

Fitting
Overfitting in Decision Tree
when tree has too many splits becomes too deep, and it makes very good predictions to training data, but does poor predictions on new data.
As the tree gets deeper, the dataset gets sliced up into leaves with fewer choices. we'll have 2¹⁰ groups by the time we get to the 10th level. That's 1024 leaves.
When we divide the tree in too many leaves, we also have fewer data in each leaf. Very few Leaves will make predictions that are quite close to actual values, but they may make very unreliable predictions for new data (because each prediction is based on only a few input samples).
Underfitting in Decision Tree
On otherside, if only few splits are performed(only 2 or 4), Resulting predictions may be far off for most input samples, even in the training data.
When a model fails to capture important distinctions and patterns in the data, & perform poorly even in training data, poorly in new data is underfitting
Overfitting or underfitting(What's preferred)? We need to hit a sweet spot between 2

Supervised Learning Models(Classifier)

Decision Tree Classifier