random forest how to use the results I used the package for random forest. It is not clear to me how to use the results. 
In  logistic regression you can have an equation as an output, in standard tree some rules. If you receive a new dataset you can apply the equation on the new data and predict an outcome (like default/no default). Or saying the customers with characteristics a and characteristics b will have a default, so you can predict the outcome before it happens. That is the scoring tecnique.
Is it possible to use random forest in a similar situation, or how would you use the results of a RF? 
my python code:
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score

#creating a test and train dataset

from sklearn.cross_validation import train_test_split

train, test = train_test_split(df, test_size = 0.3)

clf = RandomForestClassifier(max_depth = 30, min_samples_split=2, n_estimators = 200, random_state = 1)

#training the model
clf.fit(train[columns], train["churn"])

#testing the model
predictions = clf.predict(test[columns])

print(predictions)

print(roc_auc_score(predictions, test["churn"]))

 A: Once you train your random forest, one could use it to 


*

*classify new data in the case of categorical variables, or predict quantitative values in the case of a continuous variable (prediction),

*quantify the relative contribution of different variables for separating groups (understanding), or 

*quantify the partial effect of individual variables on group identity (understanding). 
Note that the last can be nonlinear in multiple dimensions. 
#Create example data with 2 groups (a & b), one variable 
#that differs between groups (m1), and the other randomly drawn (m2)    
dat <- data.frame(group = c(rep('a' , 50), rep('b', 50)), 
            m1 = c(sample(1:10, 50, replace = TRUE), 
                sample(5:20, 50, replace = TRUE)),
            m2 = c(sample(1:50, 100, replace = TRUE)))


library(randomForest)

#Train random forest 
rf <- randomForest(group ~ m1 + m2, data = dat,importance = TRUE, 
                   ntree = 1001)

#Create new data to be classified
newdats <- data.frame(m1 = sample(1:20, 10), m2 = sample(1:50, 10))

#1. Predict the group identities of the new data points
predict(rf, newdats)

#2. Examine which variables are useful in separating the 2 groups
varImpPlot(rf)

#3. Plot the partial effect of each variable on the estimated group identity 
# (i.e. partial dependence plots)
library(pdp)
partial(rf, pred.var = 'm1', plot = TRUE)
partial(rf, pred.var = 'm2', plot = TRUE)

A: Random forests is or random decision forests is just an ensemble learning method for classification, regression and other tasks, which is constructed by a multiple of decision trees at training time to output the class or mean prediction of the individual trees. 
would be easier if you post some of your implementation plus the data income and outcome 
A: Im new ML, but this is my understanding (please feel free to correct):
-rfecv can be used to find most important variables.
-Using OLS on these results and removing high VIF variables, can help identify the most significant variables.
-The OS also give the coefficients rom which an equation can be developed for regression tasks to predict the target variable.
