pyspark logistic regression example

1 Basic Concepts 1) definition Gradient Descent method is to use negative gradient direction to determine the new search direction of each iteration, so that each iteration can reduce the objective function to be optimized gradually . The gradient descent method is the steepest descent method under the 2 norm. A simple form of the steepest descent method is: X (k+1) =x (k)-a*g (k), where a is called the learning rate, which can be a smaller constant. G (k) is the gradient of X (k). The gradient

clicked by users, place the advertisement most likely to be clicked by the user where the user can see it, and then ask him to "Click me!" Once the user clicks, you will have money to accept it. This is why our computers are spreading advertisements. There are also similar possibilities for a user to buy a certain item, and the possibility of a patient suffering from a certain disease. This world is random (except for human deterministic systems, of course, there may also be noise or incorrect

dimensions overlay and change amplitude around 0, and is non-linear change. And at a very large or very small time, almost unchanged, which is based on the probability of a recognition and needs. An example of sensibility, think of the extent to which your study effort has increased from 60 points to 80 points and 80 to 100 points is not linear. (3) The cost function to be formed after the formula of this relationship is a convex function.So we chose

Regression Classic linear regression: the predicted value Y is continuous. Given X and the parameter, the probability distribution of Y follows the Gaussian distribution (corresponding to the first assumption of building GLM ). According to the relationship between the Gaussian distribution and the exponential family distribution above, we can see that the model can be expressed:

application, the learning rate can be adjusted according to the specific situation. There is data to show that at that time, the above algorithm converges. Because it is difficult to calculate efficiently, it is often used instead.3. Logistic regressionThe linear regression model is no longer suitable when the dependent variable can only be evaluated in {0,1}, because the presence of extreme data makes the

optimal solutions may be found. Therefore, in logistic regression, the loss function defined below is generally used. We assume that the probability of y=1 is that, because it's a two classification problem, the probability of y=0 is that we'll take the logarithm and multiply it by Y, and then add up all the samples:We hope that the logistic

Reprinted from: http://blog.csdn.net/zouxy09/article/details/20319673First, logistic regression (logisticregression)Logistic regression (logistic regression) is the most commonly used machine learning method in the industry to est

Objective:In life, people often encounter various optimization problems, such as how to get from one location to another in the shortest time. How can you get the most benefit from the least amount of money you have invested? How to design a chip so that it consumes the lowest power and the best performance? In this section, we will learn an optimization algorithm--logistic regression, the purpose of design

how much hope, you have to try again how many times, the enemy victorious, haha. The Logistic regression is so gentle that what it gives us is the likelihood that your sample belongs to the positive class.and some math. (For more understanding, see References) Suppose our sample is {x, Y},y is 0 or 1, denotes a positive or negative class, andx is our sample eigenvector of M-dimensional. Then this sample x

) expectations, by the above calculation we know T (y) =y , and Y's expectation is the normal distribution of the parameter μ; by the above calculation we know μ=η, and η=θt x. Therefore, linear regression is a special case of generalized linear regression, and its model is:Logistic Regression Logistic

classify the classification boundary line is to set up the regression formula according to the existing data. The word "regression" here stems from the best fit, meaning that the mathematical analysis behind it will be described in the next section to find the best fitting set of parameters . The practice of training classifiers is to find the best fit parameters, using the optimization algorithm . Next, w

Linear regressionRegression is the estimation of unknown parameters of a known formula. For example, the known formula is y=a∗x+b, the unknown parameter is a and B, using the multi-True (x, y) The training data is automatically estimated for the values of A and B. The estimated method is that after a given training sample point and a known formula, for one or more unknown parameters, the machine automatically enumerates all possible values of the par

this formula, if:(1) m is the total number of samples, that is, each iteration of the update to consider all samples, then called batch gradient descent (BGD), this method is very easy to obtain the global optimal solution, but when the number of samples, the training process is very slow. Use it when the number of samples is small.(2) when m = 1, that is, each iteration is updated to consider only one sample, the formula is called Random gradient descent (SGD), this method is characterized by

also according to the model, judging someone belongs to a disease or a certain situation of the probability of how much, that is to see how much this person is likely to belong to a disease.Logistic regression is mainly used in epidemiology, and the common situation is to explore the risk factors of a disease, predict the probability of occurrence of a disease according to the risk factors, and so on. For example