Machine Learning
- 1. Machine Learning By: Dhananjay Birmole
- 2. INTRODUCTION • Machine learning is a branch of artificial intelligence • Machine learning name is derived from the concept that it deals with “construction and study of systems that can learn from data” • Machine learning can be seen as building blocks to make computers learn to behave more intelligently • it is a theoretical concept
- 5. Regression • A regression problem is when the output variable is a real or continuous value. • It is a measure of the relation between the mean value of one variable (e.g. output) and corresponding values of other variables. • Regression analysis is a statistical process for estimating the relationships among variables. • Regression means to predict the output value using training data. • Common function used are MSE, Gradient Descent
- 6. Classification • A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”. • Classification is technique to categorize our data into a desired and distinct number of classes where we can assign label to each class. • Classification means to divide the output using training data. • Works on discrete values • Common function used are Bayer classifier, Support Vector Machine
- 7. Classification vs Regression• Classification means to group the output into a class. • Classification to predict the type of tumor i.e. harmful or not harmful using training data • If it is discrete/categorical variable, then it is classification problem • Regression means to predict the output value using training data. • Regression to predict the house price from training data • If it is a real number/continuous, then it is regression problem.
- 8. Clustering • An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labeled responses. • It is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. • It is basically a collection of objects on the basis of similarity and dissimilarity between them. • Common function used is K-mean
- 9. Decision Tree Learning •Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. •To classify a new instance, we start at the root and traverse the tree to reach a leaf; at an internal node we evaluate the predicate(or function) on the data instance, to find which child to go. The process continues till we reach a leaf node
- 10. • The decision tree in Figure3 classifies whether a person will buy a sports car or a minivan depending on their age and marital status. If the person is over 30 years and is not married, we walk the tree as follows : ‘over 30 years?’ -> yes -> ’married?’ -> no. Hence, the model outputs a sportscar.
- 11. Random Forest •Random Forest is a trademark term for an ensemble of decision trees. In Random Forest, we’ve collection of decision trees (so known as “Forest”). To classify a new object based on attributes, each tree gives a classification and we say the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest).
- 12. •Each tree is planted & grown as follows: •If the number of cases in the training set is N, then sample of N cases is taken at random but with replacement. This sample will be the training set for growing the tree. •If there are M input variables, a number m<<M is specified such that at each node, m variables are selected at random out of the M and the best split on these m is used to split the node. The value of m is held constant during the forest growing. •Each tree is grown to the largest extent possible. There is no pruning.
- 13. True vs. False and Positive vs. Negative •The Boy Who Cried Wolf example •"Wolf" is a positive class. •"No wolf" is a negative class. •We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:
- 15. •A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class. •A false positive is an outcome where the model incorrectly predicts the positive class. And a false negative is an outcome where the model incorrectly predicts the negative class. •In the following sections, we'll look at how to evaluate classification models using metrics derived from these four outcomes.
- 16. Linear Regression •In ML, we have a set of input variables (x) that are used to determine the output variable (y). A relationship exists between the input variables and the output variable. The goal of ML is to quantify this relationship. Fig 1
- 17. •In Linear Regression, the relationship between the input variables (x) and output variable (y) is expressed as an equation of the form y = a + bx. Thus, the goal of linear regression is to find out the values of coefficients a and b. Here, a is the intercept and b is the slope of the line. •Figure 1 shows the plotted x and y values for a dataset. The goal is to fit a line that is nearest to most of the points. This would reduce the distance (‘error’) between the y value of a data point and the line.
- 18. Logistic Regression • Linear regression predictions are continuous values (rainfall in cm),logistic regression predictions are discrete values (whether a student passed/failed) • Logistic regression is best suited for binary classification (datasets where y = 0 or 1, where 1 denotes the default class. Example: In predicting whether an event will occur or not, the event that it occurs is classified as 1. In predicting whether a person will be sick or not, the sick instances are denoted as 1). It is named after the transformation function used in it, called the logistic function h(x)= 1/ (1 + ex), which is an S-shaped curve.after applying a transformation function.
- 19. • In logistic regression, the output is in the form of probabilities of the default class (unlike linear regression, where the output is directly produced). As it is a probability, the output lies in the range of 0-1. The output (y-value) is generated by log transforming the x-value, using the logistic function h(x)= 1/ (1 + e^ -x) . A threshold is then applied to force this probability into a binary classification.
- 21. Future of Machine Learning •Machine Learning can be a competitive advantage to any company be it a top MNC or a startup as things that are currently being done manually will be done tomorrow by machines. Machine Learning revolution will stay with us for long and so will be the future of Machine Learning.