Tree Based Methods in Machine Learning

algorithm.

Algorithm 1: Algo 1

Data: A set of training data, Q with N elements has been established while K times do Create a random subset of N ′ data by sampling from Q containing the N samples; N′ < N; Execute algorithm Algo 2; Return all N′ back to Q

Algorithm 2: Algo 2

Choose a learner $h_m$; while K times do Pick a training set and train with $h_m$;

of which has either $1, 3, 6, 10, 15, 21, 28, 36, \hspace{0.1em}\text{or} \hspace{0.1em} 45 \hspace{0.5em} \text{balls}$.

1. We draw several samples from this corpus as presented in Fig.6.3 where in each sample is equiprobable. What type of sampling approach is being utilized here?
2. Two samples are drawn one after the other. In which of the following cases is the covariance between the two samples equals zero? - (i) Sampling without replacement - (ii) Sampling with replacement
3. During training, the corpus sampled with replacement and is divided into several folds as presented in Fig. 6.4.

1. Majority voting for binary classification
2. Weighted majority voting for binary classification
3. Majority voting for class probabilities (iv) Weighted majority class probabilities
4. An algebraic weighted average for class probabilities
5. An adaptive weighted majority voting for combining multiple classifiers

l=[]
for i,f in enumerate(filelist):
    temp = pd.read_csv(f)
    l.append(temp)
arr = np.stack(l,axis=-1)
avg_results = pd.DataFrame(arr[:,:-1,:].mean(axis=2))
avg_results['image'] = l[0]['image']
avg_results.columns = l[0].columns