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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Deep Learning vs Machine Learning for Regression

ayush
ayush Last Updated : 22 Mar, 2022
4 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Deep learning is the subfield of machine learning which uses a set of neurons organized in layers. A deep learning model consists of three layers: the input layer, the output layer, and the hidden layers. Deep learning offers several advantages over popular machine learning algorithms like k nearest neighbour, support vector machine, linear regression, etc. Unlike machine learning algorithms, deep learning models can create new features from a limited set of information and perform advanced analysis. A deep learning model can learn far more complex features than machine learning algorithms. However, despite its advantages, it also brings several challenges. These challenges include the need for a large amount of data and specialized hardware like GPUs and TPUs.

In this article, we will be creating a deep learning regression model to predict home prices using the famous Boston home price prediction dataset. Not limited to that, we will also compare its results with some renowned machine learning algorithms in various terms.

Boston House Prediction Dataset

deep learning regression model
Source: youtube.com

The dataset consists of 506 rows with 13 features and a target column, the price column. The dataset is readily available on the internet, and you can use this link to download it. Or it can also be loaded using Keras, as you will see in the below steps.

import pandas as pd
import numpy as np
#
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
(train_features, train_labels), (test_features, test_labels) = keras.datasets.boston_housing.load_data()
pd.DataFrame(train_features).head()

If we load the data from Keras, we will get data in a NumPy array. So, to better look at it, we will convert it to a pandas data frame and see the head of the data frame.

Deep Learning Regression Model

Now we have seen what the dataset looks like; we can start building the deep learning regression model. We will use the TensorFlow library to create the model. As the dataset is not very huge, we can limit the number of layers in the deep learning model and save time. We will use two fully connected layers with the relu activation. The relu activation outputs the input directly if it is greater than 0; otherwise returns zero.

model = keras.Sequential()
model.add(Dense(20, activation='relu', input_shape=[len(train_features[0])]))
model.add(Dense(1))
#
model.compile(optimizer= 'adam', loss='mse', metrics=['mse'])
history = model.fit(train_features, train_labels, epochs=20, verbose=0)

In the above code, we have created a feed-forward neural network. We trained the model for 20 epochs and used mean squared error as the loss function with adam optimizer. Now we can predict the results on the test data and compare it with actual values.

from sklearn.metrics import mean_squared_error as mse
pred = model.predict(test_features)
mse(pred, test_labels)

You can use other values sets like mean absolute error instead of mean squared error or RMS optimizer instead of adam optimizer. You can also tweak the learning rate and number of epochs to achieve better results.

Our deep learning regression model is completed, and as promised, we will compare its results with some popular machine learning algorithms.

K Nearest Neighbours

Firstly, we will train the famous K nearest neighbour regressor data. The algorithm is popular for classification problems but also gives fair results on regression tasks. It calculates the distance between examples present in the training and test sets. And then makes a prediction based on K examples, closest to the problem.

from sklearn.neighbors import KNeighborsRegressor as KNN
model_k = KNN(n_neighbors=3)
model_k.fit(train_features,train_labels)
pred_k = model_k.predict(test_features)
mse(pred_k, test_labels)

Linear Regression

A supervised learning algorithm expects a linear relationship between the input and the output variable. It works on the formula Y = a +bX. Here, X is the explanatory variable, Y is the dependent variable, and b is the slope of the best fit line.

from sklearn.linear_model import LinearRegression
model_l = LinearRegression()
model_l.fit(train_features, train_labels)
pred_l = model_l.predict(test_features)
mse(pred_l, test_labels)

Support Vector Machine

It is a supervised learning algorithm that finds a hyperplane in N-dimensional space and classifies the data points distinctly. A hyperplane is the best line to separate classes and segregate them into N-dimensional space. SVM can also be used with data that is not linearly separable.

from sklearn.svm import SVR
model_s = SVR(C=1.0)
model_s.fit(train_features, train_labels)
pred_s = model_s.predict(test_features)
mse(pred_s, test_labels)

Sklearn also provides a regularization parameter C along with SVM. Regularization prevents the model from learning many complicated features that can result in overfitting the data.

Now comes the exciting part where we will compare every model we have created so far. We will compare the models based on root mean square error.

Model Algorithm MSE(Mean Squared Error) Time-taken to train(s)
model Feed Forward Neural Network 119.768 2.929
model_k KNN 41.428 0.008
model_l Linear Regression 23.195 0.063
model_s SVM 66.345 0.305

From the table, we can derive several conclusions. With a more significant error, the deep learning model took more time to train than the machine learning algorithm. This might be due to the simplicity of the architecture or the lack of training data. The linear regression model gives the slightest mistake, which means a perfect linear relationship between the input and the target variable. I also trained SVM without the regularisation parameter, showing almost the same result. This means all the features in the dataset correlated with the target variable.

Conclusion

Deep learning offers several advantages over machine learning but can’t replace it with simple problems. This article created regression models using both deep learning and simple machine learning algorithms. We saw that training a deep learning model might not be the best choice every time from the results. For the dataset we chose, even simpler machine learning algorithms outperformed. Hence, we can conclude that deep learning should be used only when the simple machine learning algorithms fail to provide satisfactory results.

I hope you enjoyed reading this article!

For more articles – click here.

Please find me on LinkedIn.

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion. 

blogathondeep learningmachine learningprice predictionregression
ayush
ayush

Hi there. Writing and exploring are some of my hobbies. I love Machine learning because of its endless applications and scope for improvement. I enjoy problem-solving and learning about new things. I believe to learn any new skill one should have the will to learn it. Ask the right questions and the rest, Google search will take care of it. In my free time, I like listening to music and jamming on my guitar.

You can connect with me on LinkedIn, and send me any suggestions or questions. I'll be happy to reply.
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