What distinguishes Continuous and Discrete variables in a machine learning model?
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Continuous and discrete variables are two types of variables used in machine learning models.
Continuous variables are numeric variables that can take on any value within a certain range. They are typically measured on a scale and can have decimal places. For example, age, height, and temperature are continuous variables. In a machine learning model, continuous variables provide a wide range of values and allow for precise measurements and calculations.
On the other hand, discrete variables are numeric or categorical variables that can only take on specific, distinct values. They are often represented as integers or categories. Examples of discrete variables include the number of siblings, the outcome of a coin toss (heads or tails), or the type of car (sedan, SUV, etc.).
it influences the choice of algorithms and techniques used for analysis. Different modeling approaches may be applied depending on the type of variable being dealt with. Continuous variables often require statistical techniques and algorithms that can handle numerical data and make predictions based on patterns. Discrete variables, on the other hand, may require methods such as classification algorithms that can group data into specific categories or make binary decisions.
Understanding the distinction between continuous and discrete variables helps data scientists and machine learning practitioners select appropriate variables, preprocess data effectively, and choose the most suitable modeling techniques to achieve accurate predictions and insights from their models.
The basic difference between discrete variables and random variables is that a discrete variable will have finite values while a random variable will have infinite values. Moreover graph of a discrete variable will be non continuous while for random variable it will be continuous.