pratical machine learning with python

pratical machine learning with python

Linear Algebra



determinant formula

determinant properties

online calculator

inverse/adjoint(adjugate) matrix

Only non-singular matrices have inverses. (det(A) != 0)

  • minor matrix
  • cofactor matrix
  • adjoint/adjugate matrix
  • inverse matrix
  • conjugate matrix


graph demo

eigenvector steps


singular value decomposition
Am*n (m!=n)

Probability Theory

random variable: discrete/continuous

  • probability mass function: pmf (possion, binomial distribution ) for discrete random variable
  • probability density function: pdf (normal,uniform) for contiunous random variable
  • cumulative distribution function: cdf for discrete+contiunous random variable

see pmf-cdf-pdf

binomial: n times Bernoulli trial, P(x=k)=C(n,k) p^k (1-p)^(n-k)

  • marginal probability
  • joint probability
  • conditional probability
  • bayes theorem

see here

Marginal probability: the probability of an event occurring (p(A)), it may be thought of as an unconditional probability. It is not conditioned on another event. Example: the probability that a card drawn is red (p(red) = 0.5). Another example: the probability that a card drawn is a 4 (p(four)=1/13).

Joint probability: p(A and B). The probability of event A and event B occurring. It is the probability of the intersection of two or more events. The probability of the intersection of A and B may be written p(A ∩ B). Example: the probability that a card is a four and red =p(four and red) = 2/52=1/26. (There are two red fours in a deck of 52, the 4 of hearts and the 4 of diamonds).

Conditional probability: p(A|B) is the probability of event A occurring, given that event B occurs. Example: given that you drew a red card, what’s the probability that it’s a four (p(four|red))=2/26=1/13. So out of the 26 red cards (given a red card), there are two fours so 2/26=1/13.

bayes theorem: p(cancer)=0.01, p(positive test|cancer)=0.9, p(positive test|no cancer)=0.08
p(cancer|positive test)?



2 types of statistics

  • descriptive statistics 描述性统计值
  • inferential statistics 推理性统计值

descriptive statistics


  • n, sum, min,max, range =max-min,
  • mean,median,mode
  • variance,standard deviation
  • skewness,kurtosis

    from mean,median,mode


  • mean: regular meaning of “average”
  • median: middle value
  • mode: most often

2 types of data set: here

  • population: u,sigma^2, sigma —> parameter
  • sample: x, s^2, s —> statistic




see example

skewness vs kurtosis

  • skewness: 偏度 the degree of symmetry
  • kurtosis: 峰度 the degree of peakedness/flatness



formula see skewness kurtosis formula

inferential statistics

inferential statistics
inferential statistics
Each hypothesis: null hypothesis + an alternative hypothesis.

  • H0: u1=u2=u3=…=un. it indicates that the group means for the various groups are NOT very different from each other based on statistical significance levels.
  • Ha: there exists at least two group means that are statistically significantly different from each other.

significance tests 显著性检验

  • H0: there is NO real difference
  • Ha: there is a difference

    Reject H0 at 5% significant level if p-value<5%, statistical significant
    Reject H0 at 1% significant level if p-value<1%, highly significant
    one-tailed tests vs two-tailed tests

one-way ANOVA test:

  • if p-value<=5%, the result is statistically significant different, we reject the null hypothesis in favor of the alternative hypothesis. (Ha was correct)
  • Otherwise, if the results is not statistically significant, we conclude that our null hypothesis was correct. (H0 was correct)

anova test

F-stat>4.737 or p-value<0.05, then reject H0

boxplot for anova test

parametric tests vs nonparametric tests 参数检验 vs 非参数检验

Data Mining

  • KDD: knowledge discovery of dataset
  • CRISP-DM: cross-industry standard process for data mining 跨行业数据挖掘标准流程


Machine Learning methods

with/without labels

  • supervised learning:
    • classification
    • regression
  • unsupervised learning
    • clustering
    • dimensionality reduction
    • anomaly detection
    • assiciation rule-mining/market basket analysis(购物篮分析)
  • semi-supervised learning
  • reinforcement learning


  • batch learning/offline learning
  • online learning


  • instance based learning
  • model based learning



  • descriptive statistics
  • inferential statistics


3 types

  • univariate analysis: n=1
  • bivariate analysis: n=2
  • multivariate analysis: n>=3

3 types of analysis

  • use histogram to visualize data
  • correlation matrix/heatmap

Model Evaluation


confusion matrix

  • accuracy
  • precision
  • recall
  • F1-score: harmonic mean 调和平均值

value range (0-1), the bigger, the better.

confusion matrix

precision vs recall curve
precision vs recall

another curve

  • roc: receiver operating characteristic 接受者操作特征. TPR vs FPR curve
  • auc: area under curve. value range (0-1), the bigger, the better.

roc basic
roc demo

all in one
roc, precision recall, f1-score

multi-class classification for ROC

  • micro-averaging: treat as binary
  • macro-averaging: equal weight
    roc for multi-class classification



  • partition/centroid based clustering: k-means,k-medoids
  • hierachical clustering: AgglomerativeClustering, affinity propagation
    • ward/single linkage
    • averate linkage
    • complete linkage
  • distribution based clustering: gaussian mixture models
  • densitity based clustering: DBSCAN, OPTICS


partition based clustering
hierachical clustering dendrogram

external validation

with labels

  • homogeneity
  • completeness
  • v-measure: harmonic mean 调和平均值
    value range (0-1), the bigger, the better.

homogeneity completeness

internal validation

no labels
2 most important traits:

  • compact groups
  • well seperated groups


  • silhouette coefficient: SC轮廓系数. value range (-1-1), the bigger, the better.
  • calinski-harabaz index: chi指数 value range >0 , the bigger, the better.

sc vs number of clusters
sc and chi



  • mean squared error: MSE
  • root mean squared error: RMSE
  • coefficient of determination (R^2):判定系数
  • coefficient of correlation (r):相关系数 value range (-1,1)

R2: value range (0,1), the bigger, the better.

for simple linear regression, R^2 = r^2

r2 formula

correlation coefficient
r demo

r2 demo
r2 demo

images from bing search.

regression analysis


  • simple linear regression
  • multiple linear regression
  • nonlinear regression


  • training dataset(sample) is representative of the population being modeled
  • x1,x2,…,xn are linearly independent. no multicollinearity 非多重共线性
  • homoscedasticity of error 同方差性: residuals being random and no any patterns

multicollinearity 多重共线性: correlation matrix
variance inflation factor (VIF)方差膨胀因子 VIFi = 1/(1-Ri^2). VIF越大,显示共线性越严重。经验判断方法表明:当0<VIF<10,不存在多重共线性;当10≤VIF<100,存在较强的多重共线性;当VIF≥100,存在严重多重共线性。
homo-scedastic(同方差) vs hetero-scedastic (异方差性): residual plot
homogeneous vs heterogeneous 同质的vs异质的

correlation matrix/heatmap



evaluation analysis

  • residual analysis
  • normality tests (Q-Q plot)正态分布检验
  • R^2


linear regression

y = kx + b, use OLS

decision tree based regression

linear vs non-linear regression:

  • linear regression
  • decision tree based regression (non-linear)

decision tree can be used for both classification and regression. CART

node splitting
for regression:

  • MSE: mean squared error
  • RMSE: root mean squared error
  • MAE: mean absolute error
  • MAPE: mean absolute percentage error

mse and mae

for classification

  • information gain(entropy): 信息增益(熵)
  • gini impurity/index: GINI 基尼不纯度
  • misclassification error:

ig and gini
bad vs good split

stoppint criteria

  • max depth
  • min samples to split internal nodes
  • max leaf nodes

    use GridSearch to search for optimal hyperparameters

decesion tree algorithms

  • CART
  • ID3
  • C4.5

ensemble learning

3 major families:

  • bagging: boostrap aggregating, boostrap sampling(自助采样法) eg. RandomForest
  • boosting: eg. Gradient Boosting Machine(GBM), AdaBoost
    • GBM variant: LightGBM, Extreme Gradient Boosting(XGBoost)
  • stacking


  • binning
  • blending
  • averaging
  • voting

see What is the difference between Bagging and Boosting?
see 集成学习-Boosting,Bagging与Stacking

boostrap aggregating/bagging
boostrap aggregating/bagging


model stacking

Model Tuning

decision trees

  • information gain: IG 信息增益
  • gini impurity: GI 基尼不纯度

bias-variance tradeoff

The main causes of error in learning are due to noise, bias and variance.

extreme cases of bias-variance

  • underfitting: higt bias, low variance
  • overfitting: lower bias, high vairance

bias-variance tradeoff


bias-variance model complexity

see learnopencv

cross validation


cross validation strategies:

  • leave one out CV: n-1 samples as train, 1 sample as validate
  • k-fold CV: split into k equal subsets. k-1 subsets as train, 1 subset as validate

    5-fold, 10-fold in pratice

hyperparameter tuning strategies

  • grid search: manually specifying the grid, parallelizable
  • randomized search: automatic

Model Interpertation


global vs local interpertation

  • global interpertation: based on the whole dataset (feature_importance, partial_dependence plot)
  • local interpertation: based on a single prediction

global interpertation

one-way partial_dependence plot

two-way partial_dependence plot

local interpertation
local interpertation

model decision surface/ hypersurface
model decision surface

Model Deployment

  • rest api
  • micro service
  • model deployment as a service, anything as a service(XAAS)

Real-world case studies

customer segmentation

clustering problem


  • geographic 地理因素
  • demographic 人口统计因素
  • psychographic 心理因素
  • behavioural 行为因素

customer segmentation

RFM Model for customer value

  • recency
  • frequency
  • monetary value

RFM Model

association-rule mining

assiciation rule-mining/market basket analysis(购物篮分析)


  • association rule: {item1,item2,item3 —> itemK}
  • itemset: {milk,bread} {beer,diaper}
  • frequent itemset: {milk,bread}


  • support = frq(X,Y)/N
  • confidence = support(X,Y)/support(X) = frq(X,Y)/frq(X)
  • lift = support(X,Y)/(support(X)*support(Y)) = N*frq(X,Y)/(frq(X)*frq(Y))

good rules: large confidence, large support, lift >1

lift(X->Y) = 0 means X and Y not occur at the same time
lift(X->Y) = 1 means X and Y are independent of each other.


  • apriori algorithm: generate all 2^k itemsets, TOO EXPENSIVE
  • FP growth: no need to generate all 2^k itemsets, use special structure FP-tree, divide-and-conquer stragety

k unique products, then 2^k itemsets.

recommender system

recommender systems/ recommendation engines

big data with pandas

how to process big data with pandas ?

import pandas as pd
for chunk in pd.read_csv(<filepath>, chunksize=<your_chunksize_here>)

read by chunk
see opening-a-20gb-file-for-analysis-with-pandas

other tools

other refs

types of recommendation engines

3 types

  • user-based recommendation engines
  • content-based recommendation engines
  • hybrid/collaborative filtering(协同过滤) recommendation engines

    based on similarity

different cases

  • popularity-based: most liked songs by all users
  • similarity-based: similar songs for given user
  • matrix factorization based: use svd to get low rand approximation of the utility matrix


  • Jaccard Index/Jaccard similarity coefficient, (0-1)
  • cosine similarity

Jaccard Distance = 1 - Jaccard Index
Jaccard Index
Jaccard Index

matrix factorization

use matrix factorization to discover latent features between two different kinds of entities

utility matrix

sparse matrix

matrix factorization

use SVD: matrix factorization, PCA

implicit feedback 隐式反馈: song play count—> likeness

recommendation engine libraries

  • scikit-surprise (Simple Python Recommendation System Engine)
  • lightfm
  • crab
  • rec_sys

time series forecasting


predictive modeling

time series analysis/forecasting:

  • traditional approaches
    • Moving Average: MV
    • Exponential Smoothing: EWMA
    • Holt-Winter EWMA
    • Box-jenkins methodologies: AR, MA, ARIMA, S-ARIMA
  • deep learning approaches: RNN, eg. LSTM
    • regression modeling (x1,x2,..x6,—>x7): many-to-one
    • sequence modeling: squence -> sequence

two domains

  • frequency domain: spectral and wavelet analysis
  • time domain: auto- and cross-correlation analysis

where to get data ?

  • Yahho
  • quandl:

tools to fetch data:

  • quandl: register for key first
  • pandas-datareader

time series components

3 major components:

  • seasonality
  • trend
  • residual

major components

smoothing techniques

  • Moving Average: MV
  • Exponential Smoothing: EWMA


AR vs MV

  • auto regressive
  • moving average

    ARIMA: auto regressive integrated moving average

key concepts

  • Stationarity(平稳性): One the key assumptions behind the ARIMA models. Stationarity refers to the property where for a time series its mean, variance, and autocorrelation are time invariant. In other words, mean, variance,and autocorrelation do not change with time
  • Differencing(差分): differencing is widely used to stabilize the mean of a time series. We can then apply different tests to confirm if the resulting series is stationary or not.
  • Unit Root Tests: Statistical tests that help us understand if a given series is stationary
    or not.
    • ad_fuller_test: The Augmented Dickey Fuller test begins with a null hypothesis of series being non-stationary
    • kpss_test: while Kwiatkowski-Phillips-Schmidt-Shin test or KPSS has a null hypothesis that the series is stationary.

ad_fuller_test 1

not statistically significant, accpet H0: non-stationary
validate 1

ad_fuller_test 2

statistically significant, reject H0 and accept Ha: stationary
validate 2

ARIMA(p,d,q) model

  • p is the order of Autoregression
  • q is the order of Moving average
  • d is the order of differencing

how to choose p and q?

  • ACF or Auto Correlation Function plot —> q = 1
  • PACF or the Partial Auto Correlation Function plot —> p = 1


use grid search to choose p and q based on AIC

AIC or Akaike Information Criterion measures the
goodness of fit and parsimony.
auto ARIMA


Efficient Market Hypothesis: which says that it is almost impossible to beat the market consistently and there
are others which disagree with it.


  • regression modeling
  • sequence modeling

regression modeling

(N,W,F) format as input

  • number of sequence
  • window: length of sequence
  • features per timestamp

for regression

for sequence

we need to pad test sequence to match input shape.

other time series tools

New Concepts

  • Linear Discriminant Analysis(LDA)线性判别分析
  • Quadratic Discriminant Analysis(QDA)线性判别分析

sklearn code

from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis



  • 20190516: created.