Machine Learning is a branch of Artificial Intelligence (AI) which deals with the computer algorithms being used on any data. It focuses on automatically learning from the data being fed into it and it gives us results by improving on the previous predictions every time.
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Top Machine Learning Algorithms Used in Python
Below are some of the top machine learning algorithms used in Python, along with code snippets shows their implementation and visualizations of classification boundaries.
1. Linear Regression
Linear regression is one of the most commonly used supervised machine learning technique. As its name suggests, this regression tries to model the relationship between two variables using a linear equation and fitting that line to the observed data. This technique is used to estimate real continuous values like total sales made, or cost of houses.
The line of best fit is also called the regression line. It is given by the following equation:
Y = a*X + b
where Y is the dependent variable, a is the slope, X is the independent variable and b is the intercept value. The coefficients a and b are derived by minimizing the square of the difference of that distance between the various data points and the regression line equation.
# synthetic dataset for simple regression
from sklearn.datasets import make_regression
plt.figure()
plt.title( ‘Sample regression problem with one input variable’ )
X_R1, y_R1 = make_regression( n_samples = 100, n_features = 1, n_informative = 1, bias = 150.0, noise = 30, random_state = 0 )
plt.scatter( X_R1, y_R1, marker = ‘o’, s = 50 )
plt.show()
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from sklearn.linear_model import LinearRegression
X_train, X_test, y_train, y_test = train_test_split( X_R1, y_R1,
random_state = 0 )
linreg = LinearRegression().fit( X_train, y_train )
print( ‘linear model coeff (w): {}’.format( linreg.coef_ ) )
print( ‘linear model intercept (b): {:.3f}’z.format( linreg.intercept_ ) )
print( ‘R-squared score (training): {:.3f}’.format( linreg.score( X_train, y_train ) ) )
print( ‘R-squared score (test): {:.3f}’.format( linreg.score( X_test, y_test ) ) )
Output
linear model coeff (w): [ 45.71]
linear model intercept (b): 148.446
R-squared score (training): 0.679
R-squared score (test): 0.492
The following code will draw the fitted regression line on the plot of our data points.
plt.figure( figsize = ( 5, 4 ) )
plt.scatter( X_R1, y_R1, marker = ‘o’, s = 50, alpha = 0.8 )
plt.plot( X_R1, linreg.coef_ * X_R1 + linreg.intercept_, ‘r-‘ )
plt.title( ‘Least-squares linear regression’ )
plt.xlabel( ‘Feature value (x)’ )
plt.ylabel( ‘Target value (y)’ )
plt.show()
Preparing a Common Dataset For Exploring Classification Techniques
The following data is going to be used to show the various classification algorithms which are most commonly used in machine learning in Python.
The UCI Mushroom Data Set is stored in mushrooms.csv.
%matplotlib notebook
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
df = pd.read_csv( ‘readonly/mushrooms.csv’ )
df2 = pd.get_dummies( df )
df3 = df2.sample( frac = 0.08 )
X = df3.iloc[:, 2:]
y = df3.iloc[:, 1]
pca = PCA( n_components = 2 ).fit_transform( X )
X_train, X_test, y_train, y_test = train_test_split( pca, y, random_state = 0 )
plt.figure( dpi = 120 )
plt.scatter( pca[y.values == 0, 0], pca[y.values == 0, 1], alpha = 0.5, label = ‘Edible’, s = 2 )
plt.scatter( pca[y.values == 1, 0], pca[y.values == 1, 1], alpha = 0.5, label = ‘Poisonous’, s = 2 )
plt.legend()
plt.title( ‘Mushroom Data Set\nFirst Two Principal Components’ )
plt.xlabel( ‘PC1’ )
plt.ylabel( ‘PC2’ )
plt.gca().set_aspect( ‘equal’ )
We will use the function defined below to get the decision boundaries of the different classifiers we’ll use on the mushroom dataset.
def plot_mushroom_boundary( X, y, fitted_model ):
plt.figure( figsize = (9.8, 5), dpi = 100 )
for i, plot_type in enumerate( [‘Decision Boundary’, ‘Decision Probabilities’] ):
plt.subplot( 1, 2, i + 1 )
mesh_step_size = 0.01 # step size in the mesh
x_min, x_max = X[:, 0].min() – .1, X[:, 0].max() + .1
y_min, y_max = X[:, 1].min() – .1, X[:, 1].max() + .1
xx, yy = np.meshgrid( np.arange( x_min, x_max, mesh_step_size ), np.arange( y_min, y_max, mesh_step_size ) )
if i == 0:
Z = fitted_model.predict( np.c_[xx.ravel(), yy.ravel()] )
else:
try:
Z = fitted_model.predict_proba( np.c_[xx.ravel(), yy.ravel()] )[:, 1]
except:
plt.text( 0.4, 0.5, ‘Probabilities Unavailable’, horizontalalignment = ‘center’, verticalalignment = ‘center’, transform = plt.gca().transAxes, fontsize = 12 )
plt.axis( ‘off’ )
break
Z = Z.reshape( xx.shape )
plt.scatter( X[y.values == 0, 0], X[y.values == 0, 1], alpha = 0.4, label = ‘Edible’, s = 5 )
plt.scatter( X[y.values == 1, 0], X[y.values == 1, 1], alpha = 0.4, label = ‘Posionous’, s = 5 )
plt.imshow( Z, interpolation = ‘nearest’, cmap = ‘RdYlBu_r’, alpha = 0.15, extent = ( x_min, x_max, y_min, y_max ), origin = ‘lower’ )
plt.title( plot_type + ‘\n’ + str( fitted_model ).split( ‘(‘ )[0] + ‘ Test Accuracy: ‘ + str( np.round( fitted_model.score( X, y ), 5 ) ) )
plt.gca().set_aspect( ‘equal’ );
plt.tight_layout()
plt.subplots_adjust( top = 0.9, bottom = 0.08, wspace = 0.02 )
2. Logistic Regression
Unlike linear regression, logistic regression deals with the estimation of discrete values (0/1 binary values, true/false, yes/no). This technique is also called logit regression. This is because it predicts the probability of an event by using a logit function to train the given data. It’s value always lies between 0 and 1 (since it is calculating a probability).
The log odds of the results is constructed as a linear combination of the predictor variable as follows:
odds = p / (1 – p) = probability of event occurring or probability of event not occurring
ln( odds ) = ln( p / (1 – p) )
logit( p ) = ln( p / (1 – p) ) = b0 + b1X1 + b2X2 + b3X3 + … + bkXk
where p is the probability of presence of a characteristic.
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit( X_train, y_train )
plot_mushroom_boundary( X_test, y_test, model )
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3. Decision Tree
This is a very popular algorithm that can be used to classify both continuous and discrete variables of data. At every step, the data is split into more than one homogenous sets based on some splitting attribute/conditions.
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier( max_depth = 3 )
model.fit( X_train, y_train )
plot_mushroom_boundary( X_test, y_test, model )
4. SVM
SVM is short for Support Vector Machines. Here the basic idea is the classify the data points by using hyperplanes for separation. The goal is the find out such a hyperplane that has the maximum distance (or margin) between the data points of both the classes or categories.
We choose the plane in such a way to take care of classifying unknown points in the future with the highest confidence. SVMs are famously used because they give high accuracy while taking up very less computational power. SVMs can also be used for regression problems.
from sklearn.svm import SVC
model = SVC( kernel = ‘linear’ )
model.fit( X_train, y_train )
plot_mushroom_boundary( X_test, y_test, model )
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5. Naïve Bayes
As the name suggests, Naïve Bayes algorithm is a supervised learning algorithm based on the Bayes Theorem. Bayes Theorem uses conditional probabilities to give you the probability of an event based on some given knowledge.
Where,
P (A | B): The conditional probability that event A occurs, given that event B has already occurred. (Also called posterior probability)
P(A): Probability of event A.
P(B): Probability of event B.
P (B | A): The conditional probability that event B occurs, given that event A has already occurred.
Why is this algorithm named Naïve, you ask? This is because it assumes that all occurrences of events are independent of each other. So each feature separately defines the class a data point belongs to, without having any dependencies among themselves. Naïve Bayes is the best choice for text categorizations. It will work sufficiently well with even small amounts of training data.
from sklearn.naive_bayes import GaussianNB
model = GaussianNB()
model.fit( X_train, y_train )
plot_mushroom_boundary( X_test, y_test, model )
5. KNN
KNN stands for K-Nearest Neighbours. It is a very wide used supervised learning algorithm which classifies the test data according to its similarities with the previously classified training data. KNN does not classify all data points during training. Instead, it just stores the dataset and when it gets any new data, it then classifies those data points based on their similarities. It does so by calculating the Euclidean distance of the K number of nearest neighbours (here, n_neighbors) of that data point.
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier( n_neighbors = 20 )
model.fit( X_train, y_train )
plot_mushroom_boundary( X_test, y_test, model )
6. Random Forest
Random forest is a very simple and diverse machine learning algorithm that uses a supervised learning technique. As you can sort of guess from the name, random forest consists of a large number of decision trees, acting as an ensemble. Each decision tree will figure out the output class of the data points and the majority class will be chosen as the model’s final output. The idea here is that more trees working on the same data will tend to be more accurate in results than individual trees.
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit( X_train, y_train )
plot_mushroom_boundary( X_test, y_test, model )
7. Multi-Layer Perceptron
Multi-Layer Perceptron (or MLP) is a very fascinating algorithm coming under the branch of deep learning. More specifically, it belongs to the class of feed-forward artificial neural networks (ANN). MLP forms a network of multiple perceptrons with at least three layers: an input layer, output layer and hidden layer(s). MLPs are able to distinguish between data that are non-linearly separable.
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Each neuron in the hidden layers uses an activation function to proceed to the next layer. Here, the backpropagation algorithm is used to actually tune the parameters and hence train the neural network. It can mostly be used for simple regression problems.
from sklearn.neural_network import MLPClassifier
model = MLPClassifier()
model.fit( X_train, y_train )
plot_mushroom_boundary( X_test, y_test, model )
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Conclusion
We can conclude that different machine learning algorithms yield different decision boundaries and hence different accuracy results in classifying the same dataset.
There is no way to declare anyone algorithm as the best algorithm for all kinds of data in general. Machine learning requires rigorous trial and errors for various algorithms to determine what works best for each dataset separately. The list of ML algorithms doesn’t obviously end here. There is a vast sea of other techniques which are waiting to be explored in the Scikit-Learn library of Python. Go ahead and train your datasets using all of those and have fun!
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