This document discusses feature engineering, which is the process of transforming raw data into features that better represent the underlying problem for predictive models. It covers feature engineering categories like feature selection, feature transformation, and feature extraction. Specific techniques covered include imputation, handling outliers, binning, log transforms, scaling, and feature subset selection methods like filter, wrapper, and embedded methods. The goal of feature engineering is to improve machine learning model performance by preparing proper input data compatible with algorithm requirements.
Exploratory Data Analysis (EDA) is used to analyze datasets and summarize their main characteristics visually. EDA involves data sourcing, cleaning, univariate analysis with visualization to understand single variables, bivariate analysis with visualization to understand relationships between two variables, and deriving new metrics from existing data. EDA is an important first step for understanding data and gaining confidence before building machine learning models. It helps detect errors, anomalies, and map data structures to inform question asking and data manipulation for answering questions.
Data cleaning involves removing or correcting errors, outliers, missing values, and inconsistencies from the raw data. Feature engineering involves creating or transforming features that can enhance the predictive power of the machine learning model.
Feature engineering is the process of selecting, manipulating and transforming raw data into features that can be used in supervised learning. It's also necessary to design and train new machine learning features so it can tackle new tasks. A “feature” is any measurable input that can be used in a predictive model.
Working with the data for Machine LearningMehwish690898
The document discusses various techniques for dimensionality reduction in machine learning. It explains that dimensionality reduction transforms high-dimensional data into a lower-dimensional representation while retaining important information. Techniques include feature selection, which selects a subset of relevant features, and feature extraction, which transforms existing features into a new set of features. Principal component analysis (PCA) is presented as a feature extraction method that finds new axes along which the data has maximum variance.
The document provides an overview of machine learning activities including data exploration, preprocessing, model selection, training and evaluation. It discusses exploring different data types like numerical, categorical, time series and text data. It also covers identifying and addressing data issues, feature engineering, selecting appropriate models for supervised and unsupervised problems, training models using methods like holdout and cross-validation, and evaluating model performance using metrics like accuracy, confusion matrix, F-measure etc. The goal is to understand the data and apply necessary steps to build and evaluate effective machine learning models.
The document discusses the key steps involved in data pre-processing for machine learning:
1. Data cleaning involves removing noise from data by handling missing values, smoothing outliers, and resolving inconsistencies.
2. Data transformation strategies include data aggregation, feature scaling, normalization, and feature selection to prepare the data for analysis.
3. Data reduction techniques like dimensionality reduction and sampling are used to reduce large datasets size by removing redundant features or clustering data while maintaining most of the information.
Python software development provides ease of programming to the developers and gives quick results for any kind of projects. Suma Soft is an expert company providing complete Python software development services for small, mid and big level companies. It holds an expertise for 19 years and is backed up by a strong patronage. To know more- https://www.sumasoft.com/python-software-development
Explore how data science can be used to predict employee churn using this data science project presentation, allowing organizations to proactively address retention issues. This student presentation from Boston Institute of Analytics showcases the methodology, insights, and implications of predicting employee turnover. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights
Principal Component Analysis (PCA) is an unsupervised learning algorithm used for dimensionality reduction. It transforms correlated variables into linearly uncorrelated variables called principal components. PCA works by considering the variance of each attribute to reduce dimensionality while preserving as much information as possible. It is commonly used for exploratory data analysis, predictive modeling, and visualization.
Feature extraction and selection are important techniques in machine learning. Feature extraction transforms raw data into meaningful features that better represent the data. This reduces dimensionality and complexity. Good features are unique to an object and prevalent across many data samples. Principal component analysis is an important dimensionality reduction technique that transforms correlated features into linearly uncorrelated principal components. This both reduces dimensionality and preserves information.
Data preprocessing is the process of preparing raw data for analysis by cleaning it, transforming it, and reducing it. The key steps in data preprocessing include data cleaning to handle missing values, outliers, and noise; data transformation techniques like normalization, discretization, and feature extraction; and data reduction methods like dimensionality reduction and sampling. Preprocessing ensures the data is consistent, accurate and suitable for building machine learning models.
Data preprocessing is required because real-world data is often incomplete, noisy, inconsistent, and in an aggregate form. The goals of data preprocessing include handling missing data, smoothing out noisy data, resolving inconsistencies, computing aggregate attributes, reducing data volume to improve mining performance, and improving overall data quality. Key techniques for data preprocessing include data cleaning, data integration, data transformation, and data reduction.
This document discusses various data reduction techniques including dimensionality reduction through attribute subset selection, numerosity reduction using parametric and non-parametric methods like data cube aggregation, and data compression. It describes how attribute subset selection works to find a minimum set of relevant attributes to make patterns easier to detect. Methods for attribute subset selection include forward selection, backward elimination, and bi-directional selection. Decision trees can also help identify relevant attributes. Data cube aggregation stores multidimensional summarized data to provide fast access to precomputed information.
How to transform and select variables/features when creating a predictive model using machine learning. To see the source code visit https://github.com/Davisy/Feature-Engineering-and-Feature-Selection
The document provides an overview of key concepts in data preprocessing including data cleaning, feature transformation, standardization and normalization. It discusses techniques such as handling missing values, binning noisy data, dimensionality reduction, discretizing continuous features, and different scaling methods like standardization, min-max scaling and robust scaling. Code examples are provided to demonstrate these preprocessing techniques on various datasets. Homework includes explaining z-score standardization and dimensionality reduction, and preprocessing the Titanic dataset through cleaning, standardization and normalization.
This document provides an overview of dimensionality reduction techniques including PCA, LDA, and KPCA. It discusses how PCA identifies orthogonal axes that capture maximum variance in the data to reduce dimensions. LDA finds linear combinations of features that maximize separation between classes. KPCA extends PCA by applying a nonlinear mapping to data before reducing dimensions, allowing it to model nonlinear relationships unlike PCA.
EDAB Module 5 Singular Value Decomposition (SVD).pptxrajalakshmi5921
1. Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes a matrix into three other matrices.
2. SVD is primarily used for dimensionality reduction, information extraction, and noise reduction.
3. Key applications of SVD include matrix approximation, principal component analysis, image compression, recommendation systems, and signal processing.
1. Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes a matrix into three other matrices.
2. SVD is primarily used for dimensionality reduction, information extraction, and noise reduction.
3. Key applications of SVD include matrix approximation, principal component analysis, image compression, recommendation systems, and signal processing.
IRJET - An User Friendly Interface for Data Preprocessing and Visualizati...IRJET Journal
This document presents a tool for preprocessing and visualizing data using machine learning models. It aims to simplify the preprocessing steps for users by performing tasks like data cleaning, transformation, and reduction. The tool takes in a raw dataset, cleans it by removing missing values, outliers, etc. It then allows users to apply machine learning algorithms like linear regression, KNN, random forest for analysis. The processed and predicted data can be visualized. The tool is intended to save time by automating preprocessing and providing visual outputs for analysis using machine learning models on large datasets.
UNIT 2: Part 2: Data Warehousing and Data MiningNandakumar P
This document provides an overview of data pre-processing techniques used in data mining. It discusses common steps in data pre-processing including data cleaning, integration, transformation, reduction, and discretization. Specific techniques covered include handling missing and noisy data, data normalization, attribute selection, dimensionality reduction, and the Apriori and FP-Growth algorithms for frequent pattern mining. The goals of data pre-processing are to improve data quality, handle inconsistencies, and prepare the data for analysis.
Manufacturing is the process of converting raw materials into finished goods through various production methods. Historically, manufacturing occurred on a small scale through apprenticeships or putting-out systems, but the Industrial Revolution led to large-scale manufacturing using machines powered by steam engines
Thermodynamics and Heat Transfer - KRCE.pptxkrceseo
Thermodynamics and Heat transfer are fundamental to mechanical engineering. These principles guide the design, analysis, and optimization of various systems.
Good Energy Haus: PHN Presents Building Electrification, A Passive House Symp...TE Studio
Tim Eian's contribution to the Passive House Network's Building Electrification Symposium on July 25, 2024.
Topics covered:
- Our Motivation to Electrify
- The Context of the Project
- The Process of Electrification
- Considerations for Electrification
- Data
- Challenges of Electrification
- Successes
- Opportunities
Reciprocating Air Compressor and its TypesAtif Razi
Air Compressors
Classification of Air Compressors
Reciprocating Air Compressor
Main Parts of Reciprocating Air Compressor
Working of Reciprocating Air Compressor
Types of Reciprocating Air Compressor
Applications of Reciprocating Air Compressor
Advantages & Disadvantages of Reciprocating Air Compressor
Structural Dynamics and Earthquake Engineeringtushardatta
Slides are prepared with a lot of text material to help young teachers to teach the course for the first time. This also includes solved problems. This can be used to teach a first course on structural dynamics and earthquake engineering. The lecture notes based on which slides are prepared are available in SCRIBD.
- Earlier Induction motors were used in applications requiring a
constant speed because variable speed applications have been
dominated by DC drives
- Conventional methods for speed control of Induction motors were
either expensive or highly inefficient
- Later the availability of thyristors, power transistors, IGBT and GTO
have allowed the development of variable speed induction motor
- Later the availability of thyristors, power transistors, IGBT and GTO
have allowed the development of variable speed induction motor
drives
- DC motors require frequent maintenance due to the presence of
commutators & brushes. Also they cannot be used in explosive & dirty
environment
- On the other hand, induction motors particularly squirrel cage are
rugged, cheaper, lighter, smaller, more efficient, requires less
maintenance and can be operated in dirty & explosive environment .
maintenance and can be operated in dirty & explosive environment .
- Due to these advantages, Three-phase induction motors are the most
common machines in industry now & more than 90% of mechanical
power used in industry is supplied by 3 phase induction motors.
- Variable speed induction motor drives are expensive than DC drives
- Application
include
fans,
blowers,
cranes,
conveyors,
traction,
underground & under water installations etc
Artificial Intelligence Imaging - medical imagingNeeluPari
10 stages of Artificial Intelligence,
Artificial intelligence (AI) has made significant advancements in the field of medical imaging, offering valuable tools and capabilities to improve diagnostics, treatment planning, and patient care. Here are several ways AI is used in medical imaging
1. Unit 4 :
Basics of Feature Engineering:
1
Silver Oal College Of Engineering AndTechnology
2. Outline
2
Feature and Feature Engineering,
Feature transformation:
Construction
Extraction,
Feature subset selection :
Issues in high-dimensional data,
key drivers,
measure
overall process
3. Feature and Feature Engineering
Prof. Monali Suthar (SOCET-CE)
3
Input in machine learning which are usually in the form of
structured columns.
Algorithms require features with some specific
characteristic to work properly.
Feature Engineering?
Feature engineering is the process of transforming raw data into
features that better represent the underlying problem to the
predictive models, resulting in improved model accuracy on unseen
data.
Goals of Feature Engineering
1. Preparing the proper input dataset, compatible with the
machine learning algorithm requirements.
2. Improving the performance of machine learning models.
4. Feature Engineering Category
4
Feature Engineering is divided into 3 broad categories:-
1. Feature Selection:
It is all about selecting a small subset of features from a large pool of
features.
We select those attributes which best explain the relationship of an
independent variable with the target variable.
There are certain features which are more important than other
features to the accuracy of the model.
It is different from dimensionality reduction because the
dimensionality reduction method does so by combining existing
attributes, whereas the feature selection method includes or excludes
those features.
Ex: Chi-squared test, correlation coefficient scores, LASSO, Ridge
regression etc.
5. Feature Engineering Category
5
II. FeatureTransformation:
It means transforming our original feature to the functions of
original features.
Ex: Scaling, discretization, binning and filling missing data values are
the most common forms of data transformation.
To reduce right skewness of the data, we use log.
III. Feature Extraction:
When the data to be processed through an algorithm is too large,
it’s generally considered redundant.
Analysis with a large number of variables uses a lot of computation
power and memory, therefore we should reduce the dimensionality
of these types of variables.
It is a term for constructing combinations of the variables.
For tabular data, we use PCA to reduce features.
For image, we can use line or edge detection.
6. Feature transformation
6
Feature transformation is the process of modifying
your data but keeping the information.
These modifications will make Machine Learning
algorithms understanding easier, which will deliver better
results.
But why would we transform our features?
data types are not suitable to be fed into a machine learning
algorithm, e.g. text, categories
feature values may cause problems during the learning process,
e.g. data represented in different scales
we want to reduce the number of features to plot and visualize
data, speed up training or improve the accuracy of a specific
model
7. Feature Engineering Techniques
7
List of Techniques
1.Imputation
2.Handling Outliers
3.Binning
4.LogTransform
5.One-Hot Encoding
6.Grouping Operations
7.Feature Split
8.Scaling
9.Extracting Date
8. Imputation Using (Mean/Median) Values
8
This works by calculating the mean/median of the
non-missing values in a column and then replacing
the missing values within each column separately
and independently from the others. It can only be
used with numeric data.
9. Pros and Cons
9
Pros:
• Easy and fast.
• Works well with small numerical datasets.
Cons:
• Doesn‟t factor the correlations between features. It
only works on the column level.
• Will give poor results on encoded categorical
features (do NOT use it on categorical features).
• Not very accurate.
• Doesn‟t account for the uncertainty in the
imputations.
10. Pros and Cons
10
Pros:
• Easy and fast.
• Works well with small numerical datasets.
Cons:
• Doesn‟t factor the correlations between features. It
only works on the column level.
• Will give poor results on encoded categorical
features (do NOT use it on categorical features).
• Not very accurate.
• Doesn‟t account for the uncertainty in the
imputations.
11. Imputation Using (Most Frequent) or
(Zero/Constant) Values:
11
Most Frequent is another statistical strategy to
impute missing values and YES!! It works with
categorical features (strings or numerical
representations) by replacing missing data with the
most frequent values within each column.
Pros:
• Works well with categorical features.
Cons:
• It also doesn‟t factor the correlations between
features.
• It can introduce bias in the data.
13. Imputation Using k-NN
13
The k nearest neighbors is an algorithm that is used
for simple classification. The algorithm uses „feature
similarity‟ to predict the values of any new data
points.
This means that the new point is assigned a value
based on how closely it resembles the points in the
training set. This can be very useful in making
predictions about the missing values by finding
the k’s closest neighbor's to the observation with
missing data and then imputing them based on the
non-missing values in the neighborhood.
14. Pros and Cons
14
Pros:
• Can be much more accurate than the mean, median
or most frequent imputation methods (It depends on
the dataset).
Cons:
• Computationally expensive. KNN works by storing
the whole training dataset in memory.
• K-NN is quite sensitive to outliers in the data (unlike
SVM)
16. Handling outlier
16
Univariate method:
Univariate analysis is the simplest form of analyzing data.
“Uni” means “one”, so in other words your data has only one
variable.
It doesn‟t deal with causes or relationships (unlike regression )
and it‟s major purpose is to describe; It takes data,
summarizes that data and finds patterns in the data.
Univariate and multivariate represent two approaches to
statistical analysis.
Univariate involves the analysis of a single variable
while multivariate analysis examines two or more variables.
Most multivariate analysis involves a dependent variable and
multiple independent variables.
17. Handling outlier with Z score
17
The Z-score is the signed number of standard deviations by which
the value of an observation or data point is above the mean value of
what is being observed or measured.
Z score is an important concept in statistics. Z score is also called
standard score. This score helps to understand if a data value is
greater or smaller than mean and how far away it is from the mean.
More specifically, Z score tells how many standard deviations away a
data point is from the mean.
The intuition behind Z-score is to describe any data point by finding
their relationship with the Standard Deviation and Mean of the
group of data points. Z-score is finding the distribution of data
where mean is 0 and standard deviation is 1 i.e. normal distribution.
Z score = (x -mean) / std. deviation
If the z score of a data point is more than 3, it indicates that the data
point is quite different from the other data points. Such a data point
can be an outlier.
18. Binning
18
Data binning, bucketing is a data pre-processing method
used to minimize the effects of small observation errors.
The original data values are divided into small intervals
known as bins and then they are replaced by a general
value calculated for that bin.
This has a smoothing effect on the input data and may
also reduce the chances of overfitting in case of small
datasets.
19. Log Transform
19
The Log Transform is one of the most popular
Transformation techniques out there.
It is primarily used to convert a skewed distribution to a
normal distribution/less-skewed distribution.
In this transform, we take the log of the values in a
column and use these values as the column instead.
20. Standard Scaler
20
The Standard Scaler is another popular scaler that is very
easy to understand and implement.
For each feature, the Standard Scaler scales the values
such that the mean is 0 and the standard deviation is 1(or
the variance).
x_scaled = x – mean/std_dev
However, Standard Scaler assumes that the distribution of
the variable is normal.Thus, in case, the variables are not
normally distributed, we either choose a different scaler
or first, convert the variables to a normal distribution and
then apply this scaler
22. One-Hot Encoding
22
A one hot encoding allows the representation of
categorical data to be more expressive.
Many machine learning algorithms cannot work with
categorical data directly.
The categories must be converted into numbers.
This is required for both input and output variables that
are categorical.
23. Feature subset selection
23
Feature Selection is the most critical pre-processing
activity in any machine learning process. It intends to
select a subset of attributes or features that makes the
most meaningful contribution to a machine learning
activity.
24. High dimensional data
24
High Dimensional refers to the high number of variables or
attributes or features present in certain data sets, more so in the
domains like DNA analysis, geographic information system (GIS),
etc. It may have sometimes hundreds or thousands of dimensions
which is not good from the machine learning aspect because it may
be a big challenge for any ML algorithm to handle that. On the other
hand, a high quantity of computational and a high amount of time
will be required. Also, a model built on an extremely high number of
features may be very difficult to understand. For these reasons, it
is necessary to take a subset of the features instead of the
full set. So we can deduce that the objectives of feature selection
are:
1. Having a faster and more cost-effective (less need for computational
resources) learning model
2. Having a better understanding of the underlying model that generates
the data.
3. Improving the efficacy of the learning model.
25. Feature subset selection methods
25
1. Wrapper methods
Wrapping methods compute models with a certain subset of
features and evaluate the importance of each feature.
Then they iterate and try a different subset of features until the
optimal subset is reached.
Two drawbacks of this method are the large computation time
for data with many features, and that it tends to overfit the
model when there is not a large amount of data points.
The most notable wrapper methods of feature selection
are forward selection, backward selection, and stepwise
selection.
26. Feature subset selection methods
26
1. Wrapper methods
Forward selection starts with zero features, then, for each
individual feature, runs a model and determines the p-value
associated with the t-test or F-test performed. It then selects
the feature with the lowest p-value and adds that to the
working model.
Backward selection starts with all features contained in the
dataset. It then runs a model and calculates a p-value
associated with the t-test or F-test of the model for each
feature.
Stepwise selection is a hybrid of forward and backward
selection. It starts with zero features and adds the one feature
with the lowest significant p-value as described above.
27. Feature subset selection methods
27
1. Filter methods
Filter methods use a measure other than error rate to
determine whether that feature is useful.
Rather than tuning a model (as in wrapper methods), a subset
of the features is selected through ranking them by a useful
descriptive measure.
Benefits of filter methods are that they have a very low
computation time and will not overfit the data.
However, one drawback is that they are blind to any
interactions or correlations between features.
This will need to be taken into account separately, which will
be explained below. Three different filter methods
are ANOVA, Pearson correlation, and variance
thresholding.
28. Feature subset selection methods
28
2. Filter methods
The ANOVA (Analysis of variance) test looks a the variation
within the treatments of a feature and also between the
treatments.
The Pearson correlation coefficient is a measure of the
similarity of two features that ranges between -1 and 1. A value
close to 1 or -1 indicates that the two features have a high
correlation and may be related.
The variance of a feature determines how much predictive
power it contains. The lower the variance is, the less
information contained in the feature, and the less value it has in
predicting the response variable.
29. Feature subset selection methods
29
3. Embedded Methods
Embedded methods perform feature selection as a part of the
model creation process.
This generally leads to a happy medium between the two
methods of feature selection previously explained, as the
selection is done in conjunction with the model tuning
process.
Lasso and Ridge regression are the two most common
feature selection methods of this type, and Decision tree also
creates a model using different types of feature selection.
30. Feature subset selection methods
30
3. Embedded Methods
Lasso Regression is another way to penalize the beta coefficients in a
model, and is very similar to Ridge regression. It also adds a penalty term
to the cost function of a model, with a lambda value that must be tuned.
The smaller number of features a model has, the lower the complexity.
from sklearn.linear_model import Lasso
lasso = Lasso()
lasso.fit(X_train,y_train)
train_score=lasso.score(X_train,y_train)
test_score=lasso.score(X_test,y_test)
coeff_used = np.sum(lasso.coef_!=0)
An important note for Ridge and Lasso regression is that all of your features must
be standardized
31. Feature subset selection methods
31
3. Embedded Methods
Ridge regression can do this by penalizing the beta coefficients of a model
for being too large. Basically, it scales back the strength of correlation with
variables that may not be as important as others. Ride Regression is done
by adding a penalty term (also called ridge estimator or shrinkage estimator)
to the cost function of the regression. The penalty term takes all of the betas
and scales them by a term lambda (λ) that must be tuned (usually with cross
validation: compares the same model but with different values of lambda).
from sklearn.linear_model import Ridge
rr = Ridge(alpha=0.01)
rr.fit(X_train, y_train)