utils.r2_performance_metric_example()
X_train, X_test, y_train, y_test = train_test_split(features,
prices,
test_size=0.2,
random_state=10)
print("Training and testing split was successful.")
utils.visulaize_learning_curves(features, prices)
utils.visulaize_model_complexity_curves(X_train, y_train)
# Fit the training data to the model using grid search
decision_tree_reg = utils.fit_model(np.asarray(X_train),
np.asarray(y_train))
# # Produce the value for 'max_depth'
print("Parameter 'max_depth' is {} for the optimal model.".format(
decision_tree_reg.get_params()['max_depth']))
features_set_to_predict = [
[5, 17, 15], # Client 1
[4, 32, 22], # Client 2
[8, 3, 12]
] # Client 3
utils.predict_house_price(decision_tree_reg, features_set_to_predict)
# Produce a matrix for client data
vs.PredictTrials(features, prices, utils.fit_model,
features_set_to_predict)
# # For the feature LSTAT or Neibhorhood poverty level the highest percentage is that of Client 2 at 32\% and this is the main reason for bringing the prices down for a 4 room dwelling to $231639.13. Even for Client 1 with a 5 room house the LSTAT of 17% has brough down the price of the property. Whereas for Client 3 the LSTAT is low at 3% and it looks like a posh locality so the house prices are significantly higher at $893,100.00 # # The PTRATIO is the Pupil to Teachers ratio which is also a factor looked at by parents before shifting to a new locality for the respective schools. The highest PTRATIO being for Client 2 which has a higher LSTAT ratio signifying that there are more poor people in the neighborhood is at 22:1 this has brought the property price for client 2 significantly down. For Client 1 although comparatively better PTRATIO than Client 2 but still being higher than Client 3 does not command premium price relative to Client 3's property. The lowest PTRATIO is that of Client 3 which has jacked up the price of the property. # # In conclusion I would say the biggest impacting features are the RM and LSTAT which sway the prices in either direction based on their inputs. PTRATIO also has some impact but I would guess it would follow the pattern with LSTAT at leas in this Client sample. # # # # ### Sensitivity # An optimal model is not necessarily a robust model. Sometimes, a model is either too complex or too simple to sufficiently generalize to new data. Sometimes, a model could use a learning algorithm that is not appropriate for the structure of the data given. Other times, the data itself could be too noisy or contain too few samples to allow a model to adequately capture the target variable — i.e., the model is underfitted. Run the code cell below to run the `fit_model` function ten times with different training and testing sets to see how the prediction for a specific client changes with the data it's trained on. # In[43]: vs.PredictTrials(features, prices, fit_model, client_data) # ### Question 11 - Applicability # *In a few sentences, discuss whether the constructed model should or should not be used in a real-world setting.* # **Hint:** Some questions to answering: # - *How relevant today is data that was collected from 1978?* # - *Are the features present in the data sufficient to describe a home?* # - *Is the model robust enough to make consistent predictions?* # - *Would data collected in an urban city like Boston be applicable in a rural city?* # **Answer: ** # # Although the data was collected in 1978 but it was scaled to current market prices. However to deploy this model in a real world setting if we are to deploy this model we need real current data maybe last 2 or three years should help gauge as to how the market has changed since then. # # From 1978 onwards a lot of things change people's preferences the buying patterns etc. New financial mortgage instruments etc. which may come in way of making simple property buying decisions. I can tell you from my case that different people do different trade-offs between locality, availability of amount of funds, Previous owner's goodwill value(Rich famous owner like an author will command higher price although in a relatively poor locality), location of property(Property near the main road is likely to fetch higher prices than one in the interior). Or a property very near a school is bound to command a premium than one away from it if children's education is the main buying criteria. Some people want privacy and like to live in a quite neighborhood and are willing to commute for school or office. This model does not take care of all these preferences of the buyers. #
# Optimal model 的 R^2 score 为0.80，是一个比较接近1点结果，所以最优模型应该是一个可以信赖点模型。 # ### 模型健壮性 # # 一个最优的模型不一定是一个健壮模型。有的时候模型会过于复杂或者过于简单，以致于难以泛化新增添的数据；有的时候模型采用的学习算法并不适用于特定的数据结构；有的时候样本本身可能有太多噪点或样本过少，使得模型无法准确地预测目标变量。这些情况下我们会说模型是欠拟合的。 # # ### 问题 12 - 模型健壮性 # # 模型是否足够健壮来保证预测的一致性？ # # **提示**: 执行下方区域中的代码，采用不同的训练和测试集执行 `fit_model` 函数10次。注意观察对一个特定的客户来说，预测是如何随训练数据的变化而变化的。 # In[80]: # 请先注释掉 fit_model 函数里的所有 print 语句 vs.PredictTrials(features, prices, fit_model, client_data) # ### 问题 12 - 回答： # # 因为这10次的测试结果都在400万左右，变化不大（变化范围在32万左右），所以模型是健壮的。 # ### 问题 13 - 实用性探讨 # *简单地讨论一下你建构的模型能否在现实世界中使用？* # # 提示：回答以下几个问题，并给出相应结论的理由： # - *1978年所采集的数据，在已考虑通货膨胀的前提下，在今天是否仍然适用？* # - *数据中呈现的特征是否足够描述一个房屋？* # - *在波士顿这样的大都市采集的数据，能否应用在其它乡镇地区？* # - *你觉得仅仅凭房屋所在社区的环境来判断房屋价值合理吗？* # ### 问题 13 - 回答：
