Since the sampling process is inherentlyrandom, we will always have different results when running the method. To be able to have the same results, orreproducibleresults, we can define a constant calledSEEDthat has the value of the meaning of life (42): SEED =42 Note:The seed can be any...
it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the resu...
In other words, β 1 ^ is an estimate for the ATE. Due to randomization, you can assign causal meaning to that estimate: you can say that the new recommender system increased watch time by 0.14 hours per day, on average. However, that result is not statistically significant. Forget the ...
Linear Regression in Julia Linear Regression is a fundamental machine learning algorithm used to predict a numeric dependent variable based on one or more independent variables. The dependent variable (Y) should be continuous. In this tutorial I explain how to build linear regression in Julia, with...
One common preprocessing step in machine learning is to center and standardize your dataset, meaning that you substract the mean of the whole numpy array from each example, and then divide each example by the standard deviation of the whole numpy array. But for picture datasets, it is simpler...
The number of input and output nodes is determined by the data, but the number of hidden layers and the number of nodes in each are free parameters that must be determined by trial and error.The demo trains the neural network, meaning the values of the weights and biases that define the...
But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables...
In imperfect multicollinearity, variables are highly correlated, but not in a perfect, one-to-one manner like in case of perfect multicollinearity. The variables may share a high correlation, meaning when one variable changes, the other tends to change as well, but it's not an exact prediction...
In many regression problems, you must test a hypothesis involving more than one variable. This option is enabled by default, meaning the algorithm tests a combination of the parameters where two or more parameters are involved. Select the option, Decrease learning rate, if you want ...
The demo trains the neural network, meaning the values of the weights and biases that define the behavior of the neural network are computed using the training data, which has known correct input and output values. After training, the demo computes the accuracy of the model on the test dat...