Machine Learning Models - My Cheat Sheet

Supervised Models

This is a small revision on advantages and disadvantages of each model, based on suggested models of Udacity’s Nanodegree in Machine Learning Engineer.

Logistic Regression

Advantages

• Don’t have to worry about features being correlated
• You can easily update your model to take in new data (unlike Decision Trees or SVM)

Disadvantages

• Deals bad with outliers
• Must have lots of incomes for each class
• Presence of multicollinearity

Decision Tree

Advantages

• Easy to understand and interpret (for some people)
• Easy to use - Doesn’t need data normalisation, dummy variables, etc
• Can handle multi-output models
• Easily handle feature interactions
• Don’t have to worry about outliers

Disadvantages

• It can be easily overfitted
• Stability —> small changes in data can lead to completely different trees
• If a class dominates, it can easily be biased
• Don’t support online learning –> you should rebuilt the tree when new data comes

Ensemble Methods

Advantages

• Harder to overfit
• Usually better perfomance than a single model

Disadvantages

• Scaling —> usually it trains several models, which can have a bad performance with larger datasets
• Hard to implement in real time platform
• Complexity increases
• Boosting delivers poor probability estimates (https://arxiv.org/ftp/arxiv/papers/1207/1207.1403.pdf)

K-nearest Neighbors

Advantages

• Little training time
• Works well with multiclass datasets
• Good for highly unusual data

Disadvantages

• Need to determine value of k (distance)
• Neighbors-based methods are known as non-generalizing machine learning methods, since they simply “remember” all of its training data
• The accuracy of KNN can be severely degraded with high-dimension data because there is little difference between the nearest and farthest neighbor.

Gaussian Naive Bayes

Advantages

• Need less training data tran models like logistic regression
• Highly scalable
• Not sensitive to irrelevant features
• Returns the degree of certanty of the answer
• Good when you need something fast and that perfoms well

Disavantages

• Can’t learn interactions between features e.g., it can’t learn that although you love movies with Brad Pitt and Tom Cruise, you hate movies where they’re together).

SVM

Advantages

• High accuracy
• Nice theoretical guarantees regarding overfitting
• Especially popular in text classification problems

Disavantages

• Memory-intensive
• Hard to interpret
• Complicated to run and tune

Stochastic Gradient Descent

Advantages

• Efficiency
• Ease implementation

Disavantages

• A lot of hyperparameters to tune
• Sensitive to feature scaling

Unupervised Models

KMeans

Advantages

• Good when you have an idea of an ideal number of clusters
• Can scale well with lots of samples, scale medium with number of clusters

Disadvantages

• Doesn’t handle missing values very well
• Can’t find clusters that aren’t circular or spherical

Choosing the value of K

For choosing the value of k cluster we can use the elbow method:

from sklearn.clusters import Kmeans
from sklearn.metrics import silhouette_score

X = pd.DataFrame(...)

possible_k_values = range(2, len(X)+1, 5)

scores = []
for k in possible_k_values:
    model = Kmeans(n_clusters=k).fit(X)
    prediction = model.predict(X)
    score = silhouette_score(X, predictions)
    scores.append((k, score))

Then find the best numbers of clusters by choosing a k that has a lower score of errors but can still be good enough for your problem.

Hierarchical Clustering

Advantages

• Resulting hierarchical representation can be very informative
• Provides an additional ability to visualize
• Especially potent when the dataset contains real hierarchical relationship (e.g. Evolutionary biology)

Disadvantages

• Sensitive to noise and outliers
• Computationally intensive O(N^2)

Implementation on Sklearn

from sklearn import cluster

X = pd.DataFrame(...)

cls = cluster.AgglomerativeClustering(n_clusters=3, linkage='ward')
labels = cls.predict(X)

Get a dendrogram from a hierarchical clustering

from scipy.cluster.hierarchy import dendogram, ward
import matplotlib.pyplot as plt

X = pd.DataFrame(...)
linkage_matrix = ward(X)

dendogram(linkage_matrix)
plt.show()

DBSCAN

Advantages:

• We don’t need to specify the number of clusters
• Flexibility in shapes and sizes of clusters
• Able to deal with noise and outliers

Disadvantages

• Border points that are reachable from two clusters is assigned to the cluster that finds it first
• Faces difficulty finding clusters of varying densities

Tips:

• Small min samples and small episilon results in many small clusters
• Small min samples and large episilon results in most points being on the same cluster
• Large min samples results in most of points being classified as noise, except on desen regions when episilon is high
• Do not use silhouetter coefficient to test this model! Recomendado

Gaussian Mixture Model

Advantages

• Soft-clustering (you can see percentages of cluster participation on each sample)
• Cluster shape flexibility

Disadvantages

• Sensitive to initialization values
• Possible to converge to a local optimum
• Slow convergence rate

General References

• Choosing a machine learning classifier
• 1
• Sklearn documentation on Neighbors
• 3
• Sklearn documentation on Stochatic Gradient Descent
• Sklearn documentation on Ensemble Methods
• Logistic Regression Wikipedia
• Logistic Regression for machine learning
• What are the advantages of logistic regression
• The disadvantages of Logistic Regression

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