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# Tutor profile: Ali S.

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Ali S.
Tutor and Senior R/Python Programmer for Data Science, Machine Learning and Spatial Analysis
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## Questions

### Subject:R Programming

TutorMe
Question:

How do you build and evaluate a Random Forest classification model using the R programming language?

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Ali S.
Answer:

There are a couple of ways to train and evaluate a Random Forest classification using the R language. As an example let's work with the caret package. For demonstration purposes we can use the iris dataset to try to predict the species of the iris flower based on features such as sepal length, sepal width, petal length and petal width. First load the caret package and the iris dataset: library(caret) data(iris) Next partition the data in two datasets, one for training (70%) and another for validation: inBuild <- createDataPartition(y = iris\$Species, p = 0.7, list = FALSE) training <- iris[inBuild,] testing <- iris[-inBuild,] Then train the model using the 'rf' method (that is, the random forest method) with 200 trees: iris.rf <- train(form = Species ~ ., data = training, method = 'rf', ntree = 200) Note that the formula asks for predicting the Species class column. We use the period (.) to tell R that we want to include all the other variables in the dataset as predictors. Next examine the trained model: iris.rf\$finalModel Finally, apply the confusionMatrix() function to evaluate the accuracy of the Random Forest model we just trained with R: confusionMatrix(predict(iris.rf, testing), testing\$Species) Note that we use the testing dataset, instead of the training dataset, to evaluate the model performance and to get a less biased estimate of model accuracy.

### Subject:Python Programming

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Question:

What do *args and **kwargs mean in Python? Why would you use them?

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Ali S.
Answer:

*args and *kwargs are a special syntax in function definition that is used for passing a variable number of arguments to a function. This is particularly helpful when we are not sure how many arguments are going to be passed to a function. *args can be used in those situations when the number of arguments is not fixed, and besides the arguments name is not relevant. *kwargs will help us to handle for any number of keyword arguments. Note however that the words args and kwargs are just conventions as they are not imposed by Python. In other words, this special syntax includes just *(asterisk) and **(double asterisk) to pass a non-keyworded, variable-length argument list, and a keyworded, variable-length argument list, respectively.

### Subject:Machine Learning

TutorMe
Question:

What is overfitting in Machine Learning and how do you prevent it?

Inactive
Ali S.
Answer:

Overfitting is a common problem in Machine Learning that happens when a model does not generalize well from our training data to unseen data. When a model overfits, it learns the expected output for every observation instead of the general distribution of the data. There are different methods to avoid overfitting. Some of the most widely used methods include: a) dividing the dataset into three groups: a training set, a test set, and a validation set; b) reducing the complexity of the model (e.g., reducing the number of independent parameters so it is much smaller than the number of observations); c) increasing the dataset size (i.e., adding more data); d) adding regularization to constrain the learning of the model (e.g., use dropout on a neural network, prune a decision tree, add a penalty to the loss function in regression); or e) force early stopping when training a learning algorithm iteratively, among other possible methods.

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