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Update randomForest.R #3

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55 changes: 51 additions & 4 deletions randomForest.R
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Original file line number Diff line number Diff line change
@@ -1,11 +1,12 @@

setwd("~/PycharmProjects/titanic")


library(caret)
library(randomForest)
library(MLmetrics)
library(e1071)
library (keras)
library(mlbench)



normalize <- function(x) {
Expand Down Expand Up @@ -68,6 +69,23 @@ rand <- sample(1:nrow(x), nrow(x))
# train is our training sample.
train = x[rand[1:nrow(x)], ]

py_config
use_python("/home/ro/anaconda3/envs/r-tensorflow/bin/python")
library(reticulate)

k = import("sklearn.ensemble")

clf = k$RandomForestClassifier(n_jobs=10)
clf = k$RandomForestClassifier(n_jobs=as.integer(10),
random_state=as.integer(0),
n_estimators=as.integer(101))
# fit the model
clf.fit = clf$fit(X = train[,-1], y= as.factor(train[,1]))
clf.decision = clf$decision_path(train[,-1])


conda_install ("/home/ro/anaconda3/envs/r-tensorflow/bin/python","sklearn")

# Create a holdout set for evaluating model performance.
# Note: cross-validation is even better than a single holdout sample.

Expand All @@ -76,8 +94,7 @@ train = x[rand[1:nrow(x)], ]
# Review the outcome variable distribution.CLASIFICACIOON
table(Y_train, useNA = "ifany")

# Set the seed for reproducibility.
set.seed(1)


RF<-randomForest(as.factor(train$target)~.,
data = train[,-1],importance=TRUE,proximity=T,ntree=500)
Expand Down Expand Up @@ -155,6 +172,36 @@ submi<-data.frame(PassengerId,Survived)
View(submi)

write.csv(submi,"Submission.csv",row.names = FALSE)





str(train)



























Expand Down