This algorithm is really important to learn about, but it is also quite complex. It does this through a process known as Gradient Descent. Please make sure to check it out, as MSE is a fundamental concept when studying regression algorithms.īy using univariate MSE, our algorithm will be able to determine whether certain parameter values work better than others, and to what extent.īut how will a linear regression model be able to converge upon optimal parameter values? Gradient Descent Since MSE is used in so many different machine learning algorithms today, I decided to create another article solely dedicated to teaching it. In order to find the best values for b_0 and b_1, the linear regressor will use a mathematical formula known as the univariate Mean Squared Error (MSE) Cost Function. The regression algorithm fine-tunes the equation by assigning numerical values to b_0 and b_1 in an attempt to minimize the cost as much as possible.
Since the line of best fit won’t go through all the points (usually), there will be a certain degree of error, called the cost, between the model and the training dataset. Now let’s talk about how a regressor can determine whether a certain line is the most accurate representation of the data. This makes sense, because the line of best fit is essentially just a line which best represents the data. If you’re a high schooler reading this, you most likely recognized that this equation is the same as y = mx + b, the slope-intercept form of a line. X_1 represents the independent variable, which, in our case, is the temperatureī_0 is a parameter (the model will try and assign a constant value to it)ī_1 is another parameter which will be “tuned” by the model Y represents the dependent variable, or in our case, the profit Take this sample training dataset, for instance: So, in order to build a linear regression model for our lemonade stand, we need to provide it with training data showing a correlation between temperature and profit margin. The dataset a machine learning model uses to find a mathematical relationship between variables is called the training dataset.
It does this by analyzing past data that shows the independent variable in correlation with the dependent variable. The two variables in the lemonade stand scenario I described before would be the temperature(the independent variable x), and the profit(the dependent variable y).īut how does a regressor(regression model) find the relationship between two variables? How Does Simple Linear Regression Work? Model RepresentationĪ simple linear regression algorithm tries to find a linear relationship between two variables. In other words, it finds the relationship between an independent and dependent variable to make future predictions. One factor that you might want to to account for is be the temperature outside, because the hotter it is, the more likely you are to make a larger profit margin.Ī simple linear regression model takes into consideration the temperature, and after some “magic” it returns an output value: the profit. Suppose you were running a lemonade stand, and you wanted to predict how much profit you would make on a given day. This article will talk about the workings behind simple linear regression, a basic machine learning model designed to predict a non-categorical output value given a set of input data.īut what does all this non-sensical jargon really mean? Let me break it down.
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