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  /Title (machine-learning-model-basics-intermediate.pdf)
  /Creator (Cheatography)
  /Author (spriiprad)
  /Subject (Machine Learning Model - Basics/Intermediate Cheat Sheet)
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    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Machine Learning Model - Basics/Intermediate Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{spriiprad} via \textcolor{DarkBackground}{\uline{cheatography.com/122548/cs/22783/}}}
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}spriiprad \\
  \uline{cheatography.com/spriiprad} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 15th May, 2020.\\
   Page {\thepage} of \pageref{LastPage}.
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  Measure your website readability!\\
  www.readability-score.com
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Supervised Vs Unsupervised Learnig}}  \tn
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{\bf{Supervised}} & {\bf{Unsupervised}} \tn 
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Used in Classification and Prediction & Dimension Reduction and clustering \tn 
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Value of outcome must be known & No outcome variable to predict or classify \tn 
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Learns from training data and applied to validation & No learning \tn 
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\begin{tabularx}{5.377cm}{X}
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{How Supervised Learning Looks}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{How Unsupervised Learning Looks}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Supervised vs Unsupervised TLDR}}  \tn
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{1. Linear Regression}}  \tn
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Type of Response & Continuous \tn 
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{\bf{Simple Regression}} & {\bf{Multiple Regression}} \tn 
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One Independent Variable Used & Multiple Independent Variable Used \tn 
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Only One Dependent Variable & Only One Dependent Variable \tn 
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\mymulticolumn{2}{x{5.377cm}}{Relationships that are significant when using simple linear regression may no longer be when using multiple linear regression and vice-versa. \newline  \newline Insignificant relationships in simple linear regression may become significant in multiple linear regression.}  \tn 
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\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{2. Logistic Regression}}  \tn
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Type of Response & \seqsplit{Categorical} &  \tn 
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\mymulticolumn{3}{x{5.377cm}}{It can be used for explanatory tasks (=profiling) or predictive tasks (=classification)} \tn 
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\mymulticolumn{3}{x{5.377cm}}{The predictors are related to the response Y via a nonlinear function called the logit} \tn 
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\mymulticolumn{3}{x{5.377cm}}{Reducing predictors can be done via variable selection} \tn 
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\mymulticolumn{3}{x{5.377cm}}{{\bf{Types}}} \tn 
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1. Binary Regression & Two \seqsplit{Categories}. & Example: Spam or Not \tn 
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2. Multinomial Logistic Regression & Three or more \seqsplit{categories}. & Example: Veg, Non-Veg, Vegan \tn 
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3. Ordinal Logistic Regression & Three or more \seqsplit{categories} & Example: Movie rating from 1 to 5 \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{3. Naive Bayes Classifier}}  \tn
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Type of Response & Categorical \tn 
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\mymulticolumn{2}{x{5.377cm}}{Probabilistic machine learning model that's used for classification task.} \tn 
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The heart of the classifier is based on the Bayes theorem. & Bayes theorem provides a way relating the likelihood of some outcome given some informative prior information. \tn 
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We can find the probability of A happening, given that B has occurred. & B is the evidence and A is the hypothesis. That is presence of one particular feature does not affect the other. \tn 
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Bayes Theorem Probability Formula & P(A/B) = (P(B|A)*P(A))/P(B) \tn 
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Naive Bayes works well when there is a large number of predictor variables & It also works when there are missing values. \tn 
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The probability estimates are not very accurate & The classifications or predictions are generally accurate. \tn 
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\mymulticolumn{2}{x{5.377cm}}{{\bf{Assumptions}}} \tn 
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1. Predictors/features work independently on the target variable. & 2. All the predictors have an equal effect on the outcome. \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{4. Neural Networks}}  \tn
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Type of Response & Both Categorical and Continuous (particularly useful) \tn 
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\mymulticolumn{2}{x{5.377cm}}{Learns complex patterns using layers of neurons which mathematically transform the data.} \tn 
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\mymulticolumn{2}{x{5.377cm}}{The layers between the input and output are referred to as "hidden layers".} \tn 
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\mymulticolumn{2}{x{5.377cm}}{Learns relationships between the features that other algorithms cannot easily discover.} \tn 
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\mymulticolumn{2}{x{5.377cm}}{{\bf{Architecture of Neural Net}}} \tn 
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Input Layer & Nodes(variables) with information from the external environment \tn 
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Output Layer & Nodes(variables) that send information to the external environment or to another element in the network \tn 
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Hidden Layer & Nodes that only communicate with other layers of the network and are not visible to the external environment \tn 
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{5. Decision Trees}}  \tn
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\mymulticolumn{2}{x{5.377cm}}{The decision tree is produced by successively cutting the data set into smaller and smaller chunks, which are increasingly "pure" in terms of the value of the target variable.} \tn 
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{\bf{Random Forest - Ensemble Method}} & {\bf{Boosted Trees - Ensemble Method}} \tn 
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Consists of a large number of individual decision trees that operate as an ensemble & Boosting is a method of converting weak learners into strong learners. \tn 
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Each individual tree in the random forest spits out a class prediction and the class with the most votes becomes our model's prediction & Boosted trees is the process of building a large, additive tree by fitting a sequence of smaller trees \tn 
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The predictions (and therefore the errors) made by the individual trees need to have low correlations with each other. & In boosting, each new tree is a fit on a modified version of the original data set. \tn 
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Random Forests train each tree independently, using a random sample of the data. & GBTs train one tree at a time, where each new tree helps to correct errors made by previously trained trees. \tn 
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{6. How KNN works}}  \tn
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{6. K-Nearest Neighbors}}  \tn
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Type of Response & Both Categorical and Continuous \tn 
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\mymulticolumn{2}{x{5.377cm}}{KNN is method for classifying objects based on their similarity to a data with known classifications.} \tn 
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\mymulticolumn{2}{x{5.377cm}}{K-Nearest Neighbors (KNN) makes a prediction for a new observation by searching for the most similar training observations and pooling (usually done by taking the mean average) their values} \tn 
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\mymulticolumn{2}{x{5.377cm}}{Training set has to be very large for this to work effectively} \tn 
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\mymulticolumn{2}{x{5.377cm}}{Redundant and/or irrelevant variables can distort the classification results; the method is sensitive to noise in the data.} \tn 
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\mymulticolumn{2}{x{5.377cm}}{Nominal variables pose problems for measuring distance} \tn 
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\mymulticolumn{2}{x{5.377cm}}{It is a non-parametric model ... does not require distribution assumptions regarding the variables and does not make statistical inferences to a population} \tn 
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\mymulticolumn{2}{x{5.377cm}}{KNN is an example of a family of algorithms known as instance-based or memory-based learning that classify new objects by their similarity to previously known objects.} \tn 
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% That's all folks
\end{multicols*}

\end{document}