By Zachary Galante — Senior Data Science Student at Bryant University
What is KNN?
KNN is a very basic Machine Learning algorithm that uses surrounding data to predict on new data. As shown in the image below by the question mark, it represents new data (or the test case) for the algorithm to classify. It then takes into account the classes and the distance of it’s neighbors make predictions for the testing data.
A combination of different approaches leads to better results: this statement works in different aspects of our life and also adapts to algorithms based on machine learning.
Stacking is the process of combining various machine learning algorithms. This technique is due to David H. Wolpert, an American mathematician, physicist, and computer scientist.
We will learn how to implement a stacking method.
from heamy.dataset import Dataset
from heamy.estimator import Regressor
from heamy.pipeline import ModelsPipeline
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
Download data file ‘traffic_data.txt’ https://github.com/appyavi/Dataset . This is a dataset that counts the number of cars passing by during baseball games at the Los Angeles Dodgers home stadium. Each line in this file contains comma-separated strings formatted in the following manner:
Let’s see how to estimate the traffic.
SVM regressor to estimate traffic
import numpy as np
from sklearn import preprocessing
from sklearn.svm import SVRinput_file = 'traffic_data.txt' …
Download the data file building_event_binary.txt, building_event_multiclass.txt from https://github.com/appyavi/Dataset.
Let’s understand the data format before we start building the model. Each line in building_event_binary.txt consists of six comma-separated strings. The ordering of these six strings is as follows:
The first five strings form the input data, and our task is to predict whether or not an event is going on in the building.
Each line in building_event_multiclass.txt consists of six comma-separated strings…
We will extract hyperparameters for a model based on an SVM algorithm using the grid search method.
Let’s see how to find optimal hyperparameters:
Datafile: download ‘data_multivar.txt’ form here: https://github.com/appyavi/Dataset
from sklearn import svm
from sklearn import model_selection
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
import pandas as pd
2. Then, we load the data:
input_file = 'data_multivar.txt'…
The idea of creating a virtual human that can converse seamlessly with a user seems daunting to most people who are just getting into artificial intelligence and looking into how utterly complex existing commercial systems are. And their fears aren’t misled - larger systems that contain a plethora of data samples and an intricate network architecture, and are responsible for providing the highest quality home assistant system are very difficult to replicate. But, creating virtual assistants at a smaller level has already been simplified to allow virtually anyone to make their own conversational persona.
Over the past decade, the University…
It would be nice to know the confidence with which we classify unknown data. When a new data point is classified into a known category, we can train the SVM to compute the confidence level of that output as well. A confidence level refers to the probability that the value of a parameter falls within a specified range of values.
We will use an SVM classifier to find the best separating boundary between a
dataset of points. In addition, we will also perform a measure of the confidence level of the results obtained.
Download the file ‘data_multivar.txt’ from https://github.com/appyavi/Dataset
We dealt with problems where we had a similar number of data points in all our classes. In the real world, we might not be able to get data in such an orderly fashion. Sometimes, the number of data points in one class is a lot more than the number of data points in other classes. If this happens, then the classifier tends to get biased. The boundary won’t reflect the true nature of your data, just because there is a big difference in the number of data points between the two classes. …
The help of new technologies should not only be channelled towards business policies and market strategies. Tools such as Artificial Intelligence can represent a ‘breakthrough’, even in the field of energy saving, in solving problems such as global warming, to the extent that Larry Fink, president and CEO of BlackRock, the world’s largest investment company, has made companies aware that investors now expect full disclosure of company performance on a range of environmental, social and governance factors.
It is true that calculating and measuring the above-mentioned performance, especially in the midst of a pandemic crisis, is not an easy task…