剛入手data science, 想著自己玩一玩kaggle，玩了新手Titanic和House Price的 項目, 覺得基本的baseline還是可以寫出來，但是具體到一些細節，以至于到能拿到的出手的成績還是需要理論分析的。

本文旨在介紹kaggle比賽到各種原理與技巧，當然一切源自于coursera，由于課程都是英文的，且都比較好理解，這里直接使用英文

• Reference
  How to Win a Data Science Competition: Learn from Top Kagglers

Features: numeric, categorical, ordinal, datetime, coordinate, text

Numeric features

All models are divided into tree-based model and non-tree-based model.

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Scaling

For example: if we apply KNN algorithm to the instances below, as we see in the second row, we caculate the distance between the instance and the object. It is obvious that dimension of large scale dominates the distance.

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Tree-based models doesn’t depend on scaling

Non-tree-based models hugely depend on scaling

How to do

sklearn:

To [0,1]
sklearn.preprocessing.MinMaxScaler
X = ( X-X.min( ) )/( X.max()-X.min() )

To mean=0, std=1
sklearn.preprocessing.StandardScaler
X = ( X-X.mean( ) )/X.std()

• if you want to use KNN, we can go one step ahead and recall that the bigger feature is, the more important it will be for KNN. So, we can optimize scaling parameter to boost features which seems to be more important for us and see if this helps

Outliers

The outliers make the model diviate like the red line.

We can clip features values between teo chosen values of lower bound and upper bound

• Rank Transformation

If we have outliers, it behaves better than scaling. It will move the outliers closer to other objects

Linear model, KNN, Neural Network will benefit from this mothod.

rank([-100, 0, 1e5]) == [0,1,2] rank([1000,1,10]) = [2,0,1]

scipy:

scipy.stats.rankdata

• Other method

• Log transform: np.log(1 + x)
• Raising to the power < 1: np.sqrt(x + 2/3)

Feature Generation

Depends on

a. Prior knowledge
b. Exploratory data analysis

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Ordinal features

Examples:

• Ticket class: 1,2,3
• Driver’s license: A, B, C, D
• Education: kindergarden, school, undergraduate, bachelor, master, doctoral

Processing

1.Label Encoding
* Alphabetical (sorted)
[S,C,Q] -> [2, 1, 3]

sklearn.preprocessing.LabelEncoder

• Order of appearance
  [S,C,Q] -> [1, 2, 3]

Pandas.factorize

This method works fine with two ways because tree-methods can split feature, and extract most of the useful values in categories on its own. Non-tree-based-models, on the other side,usually can’t use this feature effectively.

2.Frequency Encoding
[S,C,Q] -> [0.5, 0.3, 0.2]

encoding = titanic.groupby(‘Embarked’).size() encoding = encoding/len(titanic) titanic[‘enc’] = titanic.Embarked.map(encoding)

from scipy.stats import rankdata

For linear model, it is also helpful.
if frequency of category is correlated with target value, linear model will utilize this dependency.

3.One-hot Encoding

pandas.get_dummies

It give all the categories of one feature a new columns and often used for non-tree-based model.
It will slow down tree-based model, so we introduce sparse matric. Most of libaraies can work with these sparse matrices directly. Namely, xgboost, lightGBM

Feature generation

Interactions of categorical features can help linear models and KNN

By concatenating string

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Datetime and Coordinates

Date and time

1.Periodicity
2.Time since

a. Row-independent moment For example: since 00:00:00 UTC, 1 January 1970;b. Row-dependent important moment Number of days left until next holidays/ time passed after last holiday.

3.Difference betwenn dates

We can add date_diff feature which indicates number of days between these events

Coordicates

1.Interesting places from train/test data or additional data

Generate distance between the instance to a flat or an old building(Everything that is meanful)

2.Aggergates statistics

The price of surrounding building

3.Rotation

Sometime it makes the model more precisely to classify the instances.

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Missing data

Hidden Nan, numeric

When drawing a histgram, we see the following picture:

It is obivous that -1 is a hidden Nan which is no meaning for this feature.

Fillna approaches

1.-999,-1,etc(outside the feature range)

It is useful in a way that it gives three possibility to take missing value into separate category. The downside of this is that performance of linear networks can suffer.

2.mean,median

Second method usually beneficial for simple linear models and neural networks. But again for trees it can be harder to select object which had missing values in the first place.

3.Reconstruct:

• Isnull

• Prediction

* Replace the missing data with the mean of medain grouped by another feature.
But sometimes it can be screwed up, like:

The way to handle this is to ignore missing values while calculating means for each category.

• Treating values which do not present in trian data

Just generate new feature indicating number of occurrence in the data(freqency)

• Xgboost can handle Nan

4.Remove rows with missing values

This one is possible, but it can lead to loss of important samples and a quality decrease.

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Text

Bag of words

Text preprocessing

1.Lowercase

2.Lemmatization and Stemming

3.Stopwords

Examples:
1.Articles(冠詞) or prepositions
2.Very common words

sklearn.feature_extraction.text.CountVectorizer:
max_df

• max_df : float in range [0.0, 1.0] or int, default=1.0
  When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words). If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None.

CountVectorizer

The number of times a term occurs in a given document

sklearn.feature_extraction.text.CountVectorizer

TFiDF

In order to re-weight the count features into floating point values suitable for usage by a classifier

• Term frequency
  tf = 1 / x.sum(axis=1) [:,None]
  x = x * tf

• Inverse Document Frequency
  idf = np.log(x.shape[0] / (x > 0).sum(0))
  x = x * idf

N-gram

sklearn.feature_extraction.text.CountVectorizer:
Ngram_range, analyzer

• ngram_range : tuple (min_n, max_n)
  The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used.

Embeddings(~word2vec)

It converts each word to some vector in some sophisticated space, which usually have several hundred dimensions

a. Relatively small vectors

b. Values in vector can be interpreted only in some cases

c. The words with similar meaning often have similar
embeddings

Example:

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轉載于:https://www.cnblogs.com/bjwu/p/8970821.html

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