Approaching (Almost) Any Machine Learning Problem
16
When we look at this data and consider it a classification problem, a lot of
algorithms come to our mind that we can apply to it, probably, we can use neural
networks. But it would be a bit of a stretch if we dive into neural networks from the
beginning. So, let’s start with something simple that we can visualize too: decision
trees.
Before we begin to understand what overfitting is, let’s divide the data into two
parts. This dataset has 1599 samples. We keep 1000 samples for training and 599
as a separate set.
Splitting can be done easily by the following chunk of code:
═════════════════════════════════════════════════════════════════════════
# use sample with frac=1 to shuffle the dataframe
# we reset the indices since they change after
# shuffling the dataframe
df = df.sample(frac=1).reset_index(drop=True)
# top 1000 rows are selected
# for training
df_train = df.head(1000)
# bottom 599 values are selected
# for testing/validation
df_test = df.tail(599)
═════════════════════════════════════════════════════════════════════════
We will now train a decision tree model on the training set. For the decision tree
model, I am going to use scikit-learn.
═════════════════════════════════════════════════════════════════════════
# import from scikit-learn
from sklearn import tree
from sklearn import metrics
# initialize decision tree classifier class
# with a max_depth of 3
clf = tree.DecisionTreeClassifier(max_depth=3)
# choose the columns you want to train on
# these are the features for the model
cols = ['fixed acidity',
'volatile acidity',
'citric acid',
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