The main focus of this seminar paper is on decision tree analysis and its applications in various industries.
A visual introduction to machine learning: In machine learning, computers apply statistical learning techniques to automatically identify patterns in data.
These techniques can be used to make highly accurate predictions. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
In machine learning terms, categorizing data points is a classification task. Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
Based on the home-elevation data to the right, you could argue that a home above 73 meters should be classified as one in San Francisco.
Adding nuance Adding another dimension allows for more nuance. For example, New York apartments can be extremely expensive per square foot. So visualizing elevation and price per square foot in a scatterplot helps us distinguish lower-elevation homes. Dimensions in a data set are called features, predictors, or variables.
Decision tree essay plotted in the green and blue regions would be in San Francisco and New York, respectively.
Identifying boundaries in data using math is the essence of statistical learning. The dataset we are using to create the model has 7 different dimensions. Creating a model is also known as training a model.
On the right, we are visualizing the variables in a scatterplot matrix to show the relationships between each pair of dimensions. There are clearly patterns in the data, but the boundaries for delineating them are not obvious.
And now, machine learning Finding patterns in data is where machine learning comes in. Machine learning methods use statistical learning to identify boundaries. One example of a machine learning method is a decision tree.
Decision trees look at one variable at a time and are a reasonably accessible though rudimentary machine learning method. Finding better boundaries Let's revisit the m elevation boundary proposed previously to see how we can improve upon our intuition.
Clearly, this requires a different perspective. By transforming our visualization into a histogram, we can better see how frequently homes appear at each elevation.
While the highest home in New York is 73m, the majority of them seem to have far lower elevations. Your first fork A decision tree uses if-then statements to define patterns in data.
For example, if a home's elevation is above some number, then the home is probably in San Francisco. In machine learning, these statements are called forks, and they split the data into two branches based on some value.
That value between the branches is called a split point. Homes to the left of that point get categorized in one way, while those to the right are categorized in another.
A split point is the decision tree's version of a boundary. Tradeoffs Picking a split point has tradeoffs. Look at that large slice of green in the left pie chart, those are all the San Francisco homes that are misclassified. These are called false negatives.
However, a split point meant to capture every San Francisco home will include many New York homes as well. The best split At the best split, the results of each branch should be as homogeneous or pure as possible. There are several mathematical methods you can choose between to calculate the best split.
As we see here, even the best split on a single feature does not fully separate the San Francisco homes from the New York ones. Recursion To add another split point, the algorithm repeats the process above on the subsets of data. This repetition is called recursion, and it is a concept that appears frequently in training models.This essay delves deeply into the origins of the Vietnam War, critiques U.S.
justifications for intervention, examines the brutal conduct of the war, and discusses the . The main focus of this seminar paper is on decision tree analysis and its applications in various industries. It also discusses about issues of decision making under uncertainty and risks involved in some of the projects.
Decision tree The decision tree is an effective way to make a business decision; because you can write out multiple alternatives and different options that will go along with these alternatives.
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Mk. 3 And why beholdest thou the mote that is in thy brother's eye, but considerest not the beam that is in thine own eye? 4 Or how wilt.