# Everything you need to know the Cox-PH Survival Model.

The ‘Survival Model’ is a method to analyze longitudinal data based on the occurrence of events and predict the time variable as a function of the events., Mathematical Representation.

The Survival model outputs a linear model which can be used to derive the probability of event occurrence and predicts the probability for each case or row in the dataset. The output can be interpreted as by looking at the variables related to the learnt model which also helps to predict the time in the future for the event to occur.

**Practice dataset/project?**

You can start uploading your own data here. Also, you can start by access our courses here.

**What is the output of the function?**

The output consists of a linear model with a log-likelihood value, number of events, coefficients of the linear model, their effectiveness, sum-of-square errors, confidence intervals and the statistics from several hypothesis tests.

**What is the next step after using the function?**

The probable next step for this function is to run the probability score on the dataset for different time periods to derive insights on the time remaining for events to occur given the training data has a lot of similarity to the validation data.

**When to use the function and what is it used for?**

This function is used when the objective is to learn a model from the data to predict the time of occurrence of an event based on the event indicator variable.

Also, read Neural Network Classifier, Naive Bayesian Classifier.

Above content is originally published at Analyttica TreasureHunt.